query: I have a basic understanding of a research direction in Question & Answering (Q&A) systems. These systems employ an integrated knowledge base, structured similarly to a database, with relational dependencies among various concepts and pre-existing answers. They interpret user queries and retrieve answers by leveraging the dependencies between concept nodes within the knowledge base. I am interested in employing a deep neural network to model this type of knowledge base, thereby eliminating the need for hard-coded, rule-based relationships between concepts. I am curious to see if such a neural network model could overcome the limitations inherent in rule-based knowledge base models. This is because all relationships and dependencies would have implicit vectorized representations, potentially enhancing computational efficiency. This approach might also augment the model's expressiveness and capacity to answer more complex questions. My goal is to demonstrate, using Q&A datasets, that my proposed model outperforms traditional Q&A models in terms of increased accuracy and faster runtime.

positive:
Most existing approaches for Knowledge Base Question Answering (KBQA) focus
on a specific underlying knowledge base either because of inherent assumptions
in the approach, or because evaluating it on a different knowledge base
requires non-trivial changes. However, many popular knowledge bases share
similarities in their underlying schemas that can be leveraged to facilitate
generalization across knowledge bases. To achieve this generalization, we
introduce a KBQA framework based on a 2-stage architecture that explicitly
separates semantic parsing from the knowledge base interaction, facilitating
transfer learning across datasets and knowledge graphs. We show that
pretraining on datasets with a different underlying knowledge base can
nevertheless provide significant performance gains and reduce sample
complexity. Our approach achieves comparable or state-of-the-art performance
for LC-QuAD (DBpedia), WebQSP (Freebase), SimpleQuestions (Wikidata) and MetaQA
(Wikimovies-KG).

positive:
Frequently asked question (FAQ) retrieval, with the purpose of providing
information on frequent questions or concerns, has far-reaching applications in
many areas, where a collection of question-answer (Q-A) pairs compiled a priori
can be employed to retrieve an appropriate answer in response to a user\u2019s
query that is likely to reoccur frequently. To this end, predominant approaches
to FAQ retrieval typically rank question-answer pairs by considering either the
similarity between the query and a question (q-Q), the relevance between the
query and the associated answer of a question (q-A), or combining the clues
gathered from the q-Q similarity measure and the q-A relevance measure. In this
paper, we extend this line of research by combining the clues gathered from the
q-Q similarity measure and the q-A relevance measure and meanwhile injecting
extra word interaction information, distilled from a generic (open domain)
knowledge base, into a contextual language model for inferring the q-A
relevance. Furthermore, we also explore to capitalize on domain-specific
topically-relevant relations between words in an unsupervised manner, acting as
a surrogate to the supervised domain-specific knowledge base information. As
such, it enables the model to equip sentence representations with the knowledge
about domain-specific and topically-relevant relations among words, thereby
providing a better q-A relevance measure. We evaluate variants of our approach
on a publicly-available Chinese FAQ dataset, and further apply and
contextualize it to a large-scale question-matching task, which aims to search
questions from a QA dataset that have a similar intent as an input query.
Extensive experimental results on these two datasets confirm the promising
performance of the proposed approach in relation to some state-of-the-art ones.

negative:
Open Domain Question Answering requires systems to retrieve external
knowledge and perform multi-hop reasoning by composing knowledge spread over
multiple sentences. In the recently introduced open domain question answering
challenge datasets, QASC and OpenBookQA, we need to perform retrieval of facts
and compose facts to correctly answer questions. In our work, we learn a
semantic knowledge ranking model to re-rank knowledge retrieved through Lucene
based information retrieval systems. We further propose a "knowledge fusion
model" which leverages knowledge in BERT-based language models with externally
retrieved knowledge and improves the knowledge understanding of the BERT-based
language models. On both OpenBookQA and QASC datasets, the knowledge fusion
model with semantically re-ranked knowledge outperforms previous attempts.

negative:
Deep learning models suffer from opaqueness. For Convolutional Neural
Networks (CNNs), current research strategies for explaining models focus on the
target classes within the associated training dataset. As a result, the
understanding of hidden feature map activations is limited by the
discriminative knowledge gleaned during training. The aim of our work is to
explain and expand CNNs models via the mirroring or alignment of CNN to an
external knowledge base. This will allow us to give a semantic context or label
for each visual feature. We can match CNN feature activations to nodes in our
external knowledge base. This supports knowledge-based interpretation of the
features associated with model decisions. To demonstrate our approach, we build
two separate graphs. We use an entity alignment method to align the feature
nodes in a CNN with the nodes in a ConceptNet based knowledge graph. We then
measure the proximity of CNN graph nodes to semantically meaningful knowledge
base nodes. Our results show that in the aligned embedding space, nodes from
the knowledge graph are close to the CNN feature nodes that have similar
meanings, indicating that nodes from an external knowledge base can act as
explanatory semantic references for features in the model. We analyse a variety
of graph building methods in order to improve the results from our embedding
space. We further demonstrate that by using hierarchical relationships from our
external knowledge base, we can locate new unseen classes outside the CNN
training set in our embeddings space, based on visual feature activations. This
suggests that we can adapt our approach to identify unseen classes based on CNN
feature activations. Our demonstrated approach of aligning a CNN with an
external knowledge base paves the way to reason about and beyond the trained
model, with future adaptations to explainable models and zero-shot learning.

negative:
Knowledge Base, represents facts about the world, often in some form of
subsumption ontology, rather than implicitly, embedded in procedural code, the
way a conventional computer program does. While there is a rapid growth in
knowledge bases, it poses a challenge of retrieving information from them.
Knowledge Base Question Answering is one of the promising approaches for
extracting substantial knowledge from Knowledge Bases. Unlike web search,
Question Answering over a knowledge base gives accurate and concise results,
provided that natural language questions can be understood and mapped precisely
to an answer in the knowledge base. However, some of the existing
embedding-based methods for knowledge base question answering systems ignore
the subtle correlation between the question and the Knowledge Base (e.g.,
entity types, relation paths, and context) and suffer from the Out Of
Vocabulary problem. In this paper, we focused on using a pre-trained language
model for the Knowledge Base Question Answering task. Firstly, we used Bert
base uncased for the initial experiments. We further fine-tuned these
embeddings with a two-way attention mechanism from the knowledge base to the
asked question and from the asked question to the knowledge base answer
aspects. Our method is based on a simple Convolutional Neural Network
architecture with a Multi-Head Attention mechanism to represent the asked
question dynamically in multiple aspects. Our experimental results show the
effectiveness and the superiority of the Bert pre-trained language model
embeddings for question answering systems on knowledge bases over other
well-known embedding methods.

negative:
Directly reading documents and being able to answer questions from them is an
unsolved challenge. To avoid its inherent difficulty, question answering (QA)
has been directed towards using Knowledge Bases (KBs) instead, which has proven
effective. Unfortunately KBs often suffer from being too restrictive, as the
schema cannot support certain types of answers, and too sparse, e.g. Wikipedia
contains much more information than Freebase. In this work we introduce a new
method, Key-Value Memory Networks, that makes reading documents more viable by
utilizing different encodings in the addressing and output stages of the memory
read operation. To compare using KBs, information extraction or Wikipedia
documents directly in a single framework we construct an analysis tool,
WikiMovies, a QA dataset that contains raw text alongside a preprocessed KB, in
the domain of movies. Our method reduces the gap between all three settings. It
also achieves state-of-the-art results on the existing WikiQA benchmark.

negative:
Information sources such as relational databases, spreadsheets, XML, JSON,
and Web APIs contain a tremendous amount of structured data that can be
leveraged to build and augment knowledge graphs. However, they rarely provide a
semantic model to describe their contents. Semantic models of data sources
represent the implicit meaning of the data by specifying the concepts and the
relationships within the data. Such models are the key ingredients to
automatically publish the data into knowledge graphs. Manually modeling the
semantics of data sources requires significant effort and expertise, and
although desirable, building these models automatically is a challenging
problem. Most of the related work focuses on semantic annotation of the data
fields (source attributes). However, constructing a semantic model that
explicitly describes the relationships between the attributes in addition to
their semantic types is critical.
  We present a novel approach that exploits the knowledge from a domain
ontology and the semantic models of previously modeled sources to automatically
learn a rich semantic model for a new source. This model represents the
semantics of the new source in terms of the concepts and relationships defined
by the domain ontology. Given some sample data from the new source, we leverage
the knowledge in the domain ontology and the known semantic models to construct
a weighted graph that represents the space of plausible semantic models for the
new source. Then, we compute the top k candidate semantic models and suggest to
the user a ranked list of the semantic models for the new source. The approach
takes into account user corrections to learn more accurate semantic models on
future data sources. Our evaluation shows that our method generates expressive
semantic models for data sources and services with minimal user input. ...

negative:
Knowledge bases contribute to many web search and mining tasks, yet they are
often incomplete. To add missing facts to a given knowledge base, various
embedding models have been proposed in the recent literature. Perhaps
surprisingly, relatively simple models with limited expressiveness often
performed remarkably well under today's most commonly used evaluation
protocols. In this paper, we explore whether recent models work well for
knowledge base completion and argue that the current evaluation protocols are
more suited for question answering rather than knowledge base completion. We
show that when focusing on a different prediction task for evaluating knowledge
base completion, the performance of current embedding models is unsatisfactory
even on datasets previously thought to be too easy. This is especially true
when embedding models are compared against a simple rule-based baseline. This
work indicates the need for more research into the embedding models and
evaluation protocols for knowledge base completion.

negative:
Can machines trace human knowledge like humans? Knowledge tracing (KT) is a
fundamental task in a wide range of applications in education, such as massive
open online courses (MOOCs), intelligent tutoring systems, educational games,
and learning management systems. It models dynamics in a student's knowledge
states in relation to different learning concepts through their interactions
with learning activities. Recently, several attempts have been made to use deep
learning models for tackling the KT problem. Although these deep learning
models have shown promising results, they have limitations: either lack the
ability to go deeper to trace how specific concepts in a knowledge state are
mastered by a student, or fail to capture long-term dependencies in an exercise
sequence. In this paper, we address these limitations by proposing a novel deep
learning model for knowledge tracing, namely Sequential Key-Value Memory
Networks (SKVMN). This model unifies the strengths of recurrent modelling
capacity and memory capacity of the existing deep learning KT models for
modelling student learning. We have extensively evaluated our proposed model on
five benchmark datasets. The experimental results show that (1) SKVMN
outperforms the state-of-the-art KT models on all datasets, (2) SKVMN can
better discover the correlation between latent concepts and questions, and (3)
SKVMN can trace the knowledge state of students dynamics, and a leverage
sequential dependencies in an exercise sequence for improved predication
accuracy.

negative:
First-order factoid question answering assumes that the question can be
answered by a single fact in a knowledge base (KB). While this does not seem
like a challenging task, many recent attempts that apply either complex
linguistic reasoning or deep neural networks achieve 65%-76% accuracy on
benchmark sets. Our approach formulates the task as two machine learning
problems: detecting the entities in the question, and classifying the question
as one of the relation types in the KB. We train a recurrent neural network to
solve each problem. On the SimpleQuestions dataset, our approach yields
substantial improvements over previously published results --- even neural
networks based on much more complex architectures. The simplicity of our
approach also has practical advantages, such as efficiency and modularity, that
are valuable especially in an industry setting. In fact, we present a
preliminary analysis of the performance of our model on real queries from
Comcast's X1 entertainment platform with millions of users every day.

negative:
Path queries on a knowledge graph can be used to answer compositional
questions such as "What languages are spoken by people living in Lisbon?".
However, knowledge graphs often have missing facts (edges) which disrupts path
queries. Recent models for knowledge base completion impute missing facts by
embedding knowledge graphs in vector spaces. We show that these models can be
recursively applied to answer path queries, but that they suffer from cascading
errors. This motivates a new "compositional" training objective, which
dramatically improves all models' ability to answer path queries, in some cases
more than doubling accuracy. On a standard knowledge base completion task, we
also demonstrate that compositional training acts as a novel form of structural
regularization, reliably improving performance across all base models (reducing
errors by up to 43%) and achieving new state-of-the-art results.

negative:
Knowledge Graphs (KGs) represent real-world noisy raw information in a
structured form, capturing relationships between entities. However, for dynamic
real-world applications such as social networks, recommender systems,
computational biology, relational knowledge representation has emerged as a
challenging research problem where there is a need to represent the changing
nodes, attributes, and edges over time. The evolution of search engine
responses to user queries in the last few years is partly because of the role
of KGs such as Google KG. KGs are significantly contributing to various AI
applications from link prediction, entity relations prediction, node
classification to recommendation and question answering systems. This article
is an attempt to summarize the journey of KG for AI.

negative:
In this work we propose a system for visual question answering. Our
architecture is composed of two parts, the first part creates the logical
knowledge base given the image. The second part evaluates questions against the
knowledge base. Differently from previous work, the knowledge base is
represented using hyper-dimensional computing. This choice has the advantage
that all the operations in the system, namely creating the knowledge base and
evaluating the questions against it, are differentiable, thereby making the
system easily trainable in an end-to-end fashion.

negative:
Multi-hop question answering (QA) requires a model to retrieve and integrate
information from different parts of a long text to answer a question. Humans
answer this kind of complex questions via a divide-and-conquer approach. In
this paper, we investigate whether top-performing models for multi-hop
questions understand the underlying sub-questions like humans. We adopt a
neural decomposition model to generate sub-questions for a multi-hop complex
question, followed by extracting the corresponding sub-answers. We show that
multiple state-of-the-art multi-hop QA models fail to correctly answer a large
portion of sub-questions, although their corresponding multi-hop questions are
correctly answered. This indicates that these models manage to answer the
multi-hop questions using some partial clues, instead of truly understanding
the reasoning paths. We also propose a new model which significantly improves
the performance on answering the sub-questions. Our work takes a step forward
towards building a more explainable multi-hop QA system.

negative:
The usage and amount of information available on the internet increase over
the past decade. This digitization leads to the need for automated answering
system to extract fruitful information from redundant and transitional
knowledge sources. Such systems are designed to cater the most prominent answer
from this giant knowledge source to the user query using natural language
understanding (NLU) and thus eminently depends on the Question-answering(QA)
field.
  Question answering involves but not limited to the steps like mapping of user
question to pertinent query, retrieval of relevant information, finding the
best suitable answer from the retrieved information etc. The current
improvement of deep learning models evince compelling performance improvement
in all these tasks.
  In this review work, the research directions of QA field are analyzed based
on the type of question, answer type, source of evidence-answer, and modeling
approach. This detailing followed by open challenges of the field like
automatic question generation, similarity detection and, low resource
availability for a language. In the end, a survey of available datasets and
evaluation measures is presented.

negative:
In order to facilitate natural language understanding, the key is to engage
commonsense or background knowledge. However, how to engage commonsense
effectively in question answering systems is still under exploration in both
research academia and industry. In this paper, we propose a novel
question-answering method by integrating multiple knowledge sources, i.e.
ConceptNet, Wikipedia, and the Cambridge Dictionary, to boost the performance.
More concretely, we first introduce a novel graph-based iterative knowledge
retrieval module, which iteratively retrieves concepts and entities related to
the given question and its choices from multiple knowledge sources. Afterward,
we use a pre-trained language model to encode the question, retrieved knowledge
and choices, and propose an answer choice-aware attention mechanism to fuse all
hidden representations of the previous modules. Finally, the linear classifier
for specific tasks is used to predict the answer. Experimental results on the
CommonsenseQA dataset show that our method significantly outperforms other
competitive methods and achieves the new state-of-the-art. In addition, further
ablation studies demonstrate the effectiveness of our graph-based iterative
knowledge retrieval module and the answer choice-aware attention module in
retrieving and synthesizing background knowledge from multiple knowledge
sources.

negative:
Question Answering is a task which requires building models capable of
providing answers to questions expressed in human language. Full question
answering involves some form of reasoning ability. We introduce a neural
network architecture for this task, which is a form of $Memory\ Network$, that
recognizes entities and their relations to answers through a focus attention
mechanism. Our model is named $Question\ Dependent\ Recurrent\ Entity\ Network$
and extends $Recurrent\ Entity\ Network$ by exploiting aspects of the question
during the memorization process. We validate the model on both synthetic and
real datasets: the $bAbI$ question answering dataset and the $CNN\ \&\ Daily\
News$ $reading\ comprehension$ dataset. In our experiments, the models achieved
a State-of-The-Art in the former and competitive results in the latter.

negative:
The intelligent question answering (IQA) system can accurately capture users'
search intention by understanding the natural language questions, searching
relevant content efficiently from a massive knowledge-base, and returning the
answer directly to the user. Since the IQA system can save inestimable time and
workforce in data search and reasoning, it has received more and more attention
in data science and artificial intelligence. This article introduced a domain
knowledge graph using the graph database and graph computing technologies from
massive heterogeneous data in electric power. It then proposed an IQA system
based on the electrical power knowledge graph to extract the intent and
constraints of natural interrogation based on the natural language processing
(NLP) method, to construct graph data query statements via knowledge reasoning,
and to complete the accurate knowledge search and analysis to provide users
with an intuitive visualization. This method thoroughly combined knowledge
graph and graph computing characteristics, realized high-speed multi-hop
knowledge correlation reasoning analysis in tremendous knowledge. The proposed
work can also provide a basis for the context-aware intelligent question and
answer.

negative:
Question answering (QA) has become a popular way for humans to access
billion-scale knowledge bases. Unlike web search, QA over a knowledge base
gives out accurate and concise results, provided that natural language
questions can be understood and mapped precisely to structured queries over the
knowledge base. The challenge, however, is that a human can ask one question in
many different ways. Previous approaches have natural limits due to their
representations: rule based approaches only understand a small set of "canned"
questions, while keyword based or synonym based approaches cannot fully
understand the questions. In this paper, we design a new kind of question
representation: templates, over a billion scale knowledge base and a million
scale QA corpora. For example, for questions about a city's population, we
learn templates such as What's the population of $city?, How many people are
there in $city?. We learned 27 million templates for 2782 intents. Based on
these templates, our QA system KBQA effectively supports binary factoid
questions, as well as complex questions which are composed of a series of
binary factoid questions. Furthermore, we expand predicates in RDF knowledge
base, which boosts the coverage of knowledge base by 57 times. Our QA system
beats all other state-of-art works on both effectiveness and efficiency over
QALD benchmarks.

negative:
Search engines based on keyword retrieval can no longer adapt to the way of
information acquisition in the era of intelligent Internet of Things due to the
return of keyword related Internet pages. How to quickly, accurately and
effectively obtain the information needed by users from massive Internet data
has become one of the key issues urgently needed to be solved. We propose an
intelligent question-answering system based on structured KB and unstructured
data, called OpenQA, in which users can give query questions and the model can
quickly give accurate answers back to users. We integrate KBQA structured
question answering based on semantic parsing and deep representation learning,
and two-stage unstructured question answering based on retrieval and neural
machine reading comprehension into OpenQA, and return the final answer with the
highest probability through the Transformer answer selection module in OpenQA.
We carry out preliminary experiments on our constructed dataset, and the
experimental results prove the effectiveness of the proposed intelligent
question answering system. At the same time, the core technology of each module
of OpenQA platform is still in the forefront of academic hot spots, and the
theoretical essence and enrichment of OpenQA will be further explored based on
these academic hot spots.

negative:
Thanks to the development of the Semantic Web, a lot of new structured data
has become available on the Web in the form of knowledge bases (KBs). Making
this valuable data accessible and usable for end-users is one of the main goals
of Question Answering (QA) over KBs. Most current QA systems query one KB, in
one language (namely English). The existing approaches are not designed to be
easily adaptable to new KBs and languages. We first introduce a new approach
for translating natural language questions to SPARQL queries. It is able to
query several KBs simultaneously, in different languages, and can easily be
ported to other KBs and languages. In our evaluation, the impact of our
approach is proven using 5 different well-known and large KBs: Wikidata,
DBpedia, MusicBrainz, DBLP and Freebase as well as 5 different languages namely
English, German, French, Italian and Spanish. Second, we show how we integrated
our approach, to make it easily accessible by the research community and by
end-users. To summarize, we provided a conceptional solution for multilingual,
KB-agnostic Question Answering over the Semantic Web. The provided first
approximation validates this concept.

negative:
Answering complex questions is a time-consuming activity for humans that
requires reasoning and integration of information. Recent work on reading
comprehension made headway in answering simple questions, but tackling complex
questions is still an ongoing research challenge. Conversely, semantic parsers
have been successful at handling compositionality, but only when the
information resides in a target knowledge-base. In this paper, we present a
novel framework for answering broad and complex questions, assuming answering
simple questions is possible using a search engine and a reading comprehension
model. We propose to decompose complex questions into a sequence of simple
questions, and compute the final answer from the sequence of answers. To
illustrate the viability of our approach, we create a new dataset of complex
questions, ComplexWebQuestions, and present a model that decomposes questions
and interacts with the web to compute an answer. We empirically demonstrate
that question decomposition improves performance from 20.8 precision@1 to 27.5
precision@1 on this new dataset.

negative:
In artificial intelligence (AI), knowledge is the information required by an
intelligent system to accomplish tasks. While traditional knowledge bases use
discrete, symbolic representations, detecting knowledge encoded in the
continuous representations learned from data has received increasing attention
recently. In this work, we propose a method for building a continuous knowledge
base (CKB) that can store knowledge imported from multiple, diverse neural
networks. The key idea of our approach is to define an interface for each
neural network and cast knowledge transferring as a function simulation
problem. Experiments on text classification show promising results: the CKB
imports knowledge from a single model and then exports the knowledge to a new
model, achieving comparable performance with the original model. More
interesting, we import the knowledge from multiple models to the knowledge
base, from which the fused knowledge is exported back to a single model,
achieving a higher accuracy than the original model. With the CKB, it is also
easy to achieve knowledge distillation and transfer learning. Our work opens
the door to building a universal continuous knowledge base to collect, store,
and organize all continuous knowledge encoded in various neural networks
trained for different AI tasks.

negative:
Within Reinforcement Learning, there is a growing collection of research
which aims to express all of an agent's knowledge of the world through
predictions about sensation, behaviour, and time. This work can be seen not
only as a collection of architectural proposals, but also as the beginnings of
a theory of machine knowledge in reinforcement learning. Recent work has
expanded what can be expressed using predictions, and developed applications
which use predictions to inform decision-making on a variety of synthetic and
real-world problems. While promising, we here suggest that the notion of
predictions as knowledge in reinforcement learning is as yet underdeveloped:
some work explicitly refers to predictions as knowledge, what the requirements
are for considering a prediction to be knowledge have yet to be well explored.
This specification of the necessary and sufficient conditions of knowledge is
important; even if claims about the nature of knowledge are left implicit in
technical proposals, the underlying assumptions of such claims have
consequences for the systems we design. These consequences manifest in both the
way we choose to structure predictive knowledge architectures, and how we
evaluate them. In this paper, we take a first step to formalizing predictive
knowledge by discussing the relationship of predictive knowledge learning
methods to existing theories of knowledge in epistemology. Specifically, we
explore the relationships between Generalized Value Functions and epistemic
notions of Justification and Truth.

negative:
The paper presents an approach to build a question and answer system that is
capable of processing the information in a large dataset and allows the user to
gain knowledge from this dataset by asking questions in natural language form.
Key content of this research covers four dimensions which are; Corpus
Preprocessing, Question Preprocessing, Deep Neural Network for Answer
Extraction and Answer Generation. The system is capable of understanding the
question, responds to the user's query in natural language form as well. The
goal is to make the user feel as if they were interacting with a person than a
machine.

negative:
Over the past few years, question answering and information retrieval systems
have become widely used. These systems attempt to find the answer of the asked
questions from raw text sources. A component of these systems is Answer
Selection which selects the most relevant from candidate answers. Syntactic
similarities were mostly used to compute the similarity, but in recent works,
deep neural networks have been used, making a significant improvement in this
field. In this research, a model is proposed to select the most relevant
answers to the factoid question from the candidate answers. The proposed model
ranks the candidate answers in terms of semantic and syntactic similarity to
the question, using convolutional neural networks. In this research, Attention
mechanism and Sparse feature vector use the context-sensitive interactions
between questions and answer sentence. Wide convolution increases the
importance of the interrogative word. Pairwise ranking is used to learn
differentiable representations to distinguish positive and negative answers.
Our model indicates strong performance on the TrecQA Raw beating previous
state-of-the-art systems by 1.4% in MAP and 1.1% in MRR while using the
benefits of no additional syntactic parsers and external tools. The results
show that using context-sensitive interactions between question and answer
sentences can help to find the correct answer more accurately.

negative:
Domain-specific community question answering is becoming an integral part of
professions. Finding related questions and answers in these communities can
significantly improve the effectiveness and efficiency of information seeking.
Stack Overflow is one of the most popular communities that is being used by
millions of programmers. In this paper, we analyze the problem of predicting
knowledge unit (question thread) relatedness in Stack Overflow. In particular,
we formulate the question relatedness task as a multi-class classification
problem with four degrees of relatedness. We present a large-scale dataset with
more than 300K pairs. To the best of our knowledge, this dataset is the largest
domain-specific dataset for Question-Question relatedness. We present the steps
that we took to collect, clean, process, and assure the quality of the dataset.
The proposed dataset Stack Overflow is a useful resource to develop novel
solutions, specifically data-hungry neural network models, for the prediction
of relatedness in technical community question-answering forums. We adopt a
neural network architecture and a traditional model for this task that
effectively utilize information from different parts of knowledge units to
compute the relatedness between them. These models can be used to benchmark
novel models, as they perform well in our task and in a closely similar task.

negative:
This paper tackles the problem of endogenous link prediction for Knowledge
Base completion. Knowledge Bases can be represented as directed graphs whose
nodes correspond to entities and edges to relationships. Previous attempts
either consist of powerful systems with high capacity to model complex
connectivity patterns, which unfortunately usually end up overfitting on rare
relationships, or in approaches that trade capacity for simplicity in order to
fairly model all relationships, frequent or not. In this paper, we propose
Tatec a happy medium obtained by complementing a high-capacity model with a
simpler one, both pre-trained separately and then combined. We present several
variants of this model with different kinds of regularization and combination
strategies and show that this approach outperforms existing methods on
different types of relationships by achieving state-of-the-art results on four
benchmarks of the literature.

negative:
Gene interaction graphs aim to capture various relationships between genes
and can represent decades of biology research. When trying to make predictions
from genomic data, those graphs could be used to overcome the curse of
dimensionality by making machine learning models sparser and more consistent
with biological common knowledge. In this work, we focus on assessing how well
those graphs capture dependencies seen in gene expression data to evaluate the
adequacy of the prior knowledge provided by those graphs. We propose a
condition graphs should satisfy to provide good prior knowledge and test it
using `Single Gene Inference' tasks. We also compare with randomly generated
graphs, aiming to measure the true benefit of using biologically relevant
graphs in this context, and validate our findings with five clinical tasks. We
find some graphs capture relevant dependencies for most genes while being very
sparse. Our analysis with random graphs finds that dependencies can be captured
almost as well at random which suggests that, in terms of gene expression
levels, the relevant information about the state of the cell is spread across
many genes.

negative:
The purpose of this paper is to describe and analyze the development of a
knowledge-based infrastructure to support MSP decision-making processes. The
paper emerged from a study to define specifications for a knowledge-based
infrastructure to provide decision support for community-level MSPs in the
Canadian province of Quebec. As part of the study, a process assessment was
conducted to understand the needs of communities as they collect, organize, and
analyze data to make decisions about their priorities. The result of this
process is a portrait, which is an epidemiological profile of health and
nutrition in their community. Portraits inform strategic planning and
development of interventions and are used to assess the impact of
interventions. Our key findings indicate ambiguities and disagreement among MSP
decision-makers regarding causal relationships between actions and outcomes,
and the relevant data needed for making decisions. MSP decision-makers
expressed a desire for easy-to-use tools that facilitate the collection,
organization, synthesis, and analysis of data, to enable decision-making in a
timely manner. Findings inform conceptual modeling and ontological analysis to
capture the domain knowledge and specify relationships between actions and
outcomes. This modeling and analysis provide the foundation for an ontology,
encoded using OWL 2 Web Ontology Language. The ontology is developed to provide
semantic support for the MSP process, defining objectives, strategies, actions,
indicators, and data sources. In the future, software interacting with the
ontology can facilitate interactive browsing by decision-makers in the MSP in
the form of concepts, instances, relationships, and axioms. Our ontology also
facilitates the integration and interpretation of community data and can help
in managing semantic interoperability between different knowledge sources.

negative:
Cold start knowledge base population (KBP) is the problem of populating a
knowledge base from unstructured documents. While artificial neural networks
have led to significant improvements in the different tasks that are part of
KBP, the overall F1 of the end-to-end system remains quite low. This problem is
more acute in personal knowledge bases, which present additional challenges
with regard to data protection, fairness and privacy. In this work, we present
a system that uses rule based annotators and a graph neural network for missing
link prediction, to populate a more complete, fair and diverse knowledge base
from the TACRED dataset.

negative:
We aim to provide table answers to keyword queries against knowledge bases.
For queries referring to multiple entities, like "Washington cities population"
and "Mel Gibson movies", it is better to represent each relevant answer as a
table which aggregates a set of entities or entity-joins within the same table
scheme or pattern. In this paper, we study how to find highly relevant patterns
in a knowledge base for user-given keyword queries to compose table answers. A
knowledge base can be modeled as a directed graph called knowledge graph, where
nodes represent entities in the knowledge base and edges represent the
relationships among them. Each node/edge is labeled with type and text. A
pattern is an aggregation of subtrees which contain all keywords in the texts
and have the same structure and types on node/edges. We propose efficient
algorithms to find patterns that are relevant to the query for a class of
scoring functions. We show the hardness of the problem in theory, and propose
path-based indexes that are affordable in memory. Two query-processing
algorithms are proposed: one is fast in practice for small queries (with small
patterns as answers) by utilizing the indexes; and the other one is better in
theory, with running time linear in the sizes of indexes and answers, which can
handle large queries better. We also conduct extensive experimental study to
compare our approaches with a naive adaption of known techniques.

negative:
Answering complex questions involving multiple entities and relations is a
challenging task. Logically, the answer to a complex question should be derived
by decomposing the complex question into multiple simple sub-questions and then
answering those sub-questions. Existing work has followed this strategy but has
not attempted to optimize the order of how those sub-questions are answered. As
a result, the sub-questions are answered in an arbitrary order, leading to
larger search space and a higher risk of missing an answer. In this paper, we
propose a novel reinforcement learning(RL) approach to answering complex
questions that can learn a policy to dynamically decide which sub-question
should be answered at each stage of reasoning. We lever-age the expected
value-variance criterion to enable the learned policy to balance between the
risk and utility of answering a sub-question. Experiment results show that the
RL approach can substantially improve the optimality of ordering the
sub-questions, leading to improved accuracy of question answering. The proposed
method for learning to order sub-questions is general and can thus be
potentially combined with many existing ideas for answering complex questions
to enhance their performance.

negative:
Building computers able to answer questions on any subject is a long standing
goal of artificial intelligence. Promising progress has recently been achieved
by methods that learn to map questions to logical forms or database queries.
Such approaches can be effective but at the cost of either large amounts of
human-labeled data or by defining lexicons and grammars tailored by
practitioners. In this paper, we instead take the radical approach of learning
to map questions to vectorial feature representations. By mapping answers into
the same space one can query any knowledge base independent of its schema,
without requiring any grammar or lexicon. Our method is trained with a new
optimization procedure combining stochastic gradient descent followed by a
fine-tuning step using the weak supervision provided by blending automatically
and collaboratively generated resources. We empirically demonstrate that our
model can capture meaningful signals from its noisy supervision leading to
major improvements over paralex, the only existing method able to be trained on
similar weakly labeled data.

negative:
Knowledge graph (KG) is known to be helpful for the task of question
answering (QA), since it provides well-structured relational information
between entities, and allows one to further infer indirect facts. However, it
is challenging to build QA systems which can learn to reason over knowledge
graphs based on question-answer pairs alone. First, when people ask questions,
their expressions are noisy (for example, typos in texts, or variations in
pronunciations), which is non-trivial for the QA system to match those
mentioned entities to the knowledge graph. Second, many questions require
multi-hop logic reasoning over the knowledge graph to retrieve the answers. To
address these challenges, we propose a novel and unified deep learning
architecture, and an end-to-end variational learning algorithm which can handle
noise in questions, and learn multi-hop reasoning simultaneously. Our method
achieves state-of-the-art performance on a recent benchmark dataset in the
literature. We also derive a series of new benchmark datasets, including
questions for multi-hop reasoning, questions paraphrased by neural translation
model, and questions in human voice. Our method yields very promising results
on all these challenging datasets.

negative:
Semantic matching is of central significance to the answer selection task
which aims to select correct answers for a given question from a candidate
answer pool. A useful method is to employ neural networks with attention to
generate sentences representations in a way that information from pair
sentences can mutually influence the computation of representations. In this
work, an effective architecture,multi-size neural network with attention
mechanism (AM-MSNN),is introduced into the answer selection task. This
architecture captures more levels of language granularities in parallel,
because of the various sizes of filters comparing with single-layer CNN and
multi-layer CNNs. Meanwhile it extends the sentence representations by
attention mechanism, thus containing more information for different types of
questions. The empirical study on three various benchmark tasks of answer
selection demonstrates the efficacy of the proposed model in all the benchmarks
and its superiority over competitors. The experimental results show that (1)
multi-size neural network (MSNN) is a more useful method to capture abstract
features on different levels of granularities than single/multi-layer CNNs; (2)
the attention mechanism (AM) is a better strategy to derive more informative
representations; (3) AM-MSNN is a better architecture for the answer selection
task for the moment.

negative:
Retrofitting is a technique used to move word vectors closer together or
further apart in their space to reflect their relationships in a Knowledge Base
(KB). However, retrofitting only works on concepts that are present in that KB.
RetroGAN uses a pair of Generative Adversarial Networks (GANs) to learn a
one-to-one mapping between concepts and their retrofitted counterparts. It
applies that mapping (post-specializes) to handle concepts that do not appear
in the original KB in a manner similar to how some natural language systems
handle out-of-vocabulary entries. We test our system on three word-similarity
benchmarks and a downstream sentence simplification task and achieve the state
of the art (CARD-660). Altogether, our results demonstrate our system's
effectiveness for out-of-knowledge and rare word generalization.

negative:
To date, most probabilistic reasoning systems have relied on a fixed belief
network constructed at design time. The network is used by an application
program as a representation of (in)dependencies in the domain. Probabilistic
inference algorithms operate over the network to answer queries. Recognizing
the inflexibility of fixed models has led researchers to develop automated
network construction procedures that use an expressive knowledge base to
generate a network that can answer a query. Although more flexible than fixed
model approaches, these construction procedures separate construction and
evaluation into distinct phases. In this paper we develop an approach to
combining incremental construction and evaluation of a partial probability
model. The combined method holds promise for improved methods for control of
model construction based on a trade-off between fidelity of results and cost of
construction.

negative:
Knowledge-based biomedical data science (KBDS) involves the design and
implementation of computer systems that act as if they knew about biomedicine.
Such systems depend on formally represented knowledge in computer systems,
often in the form of knowledge graphs. Here we survey the progress in the last
year in systems that use formally represented knowledge to address data science
problems in both clinical and biological domains, as well as on approaches for
creating knowledge graphs. Major themes include the relationships between
knowledge graphs and machine learning, the use of natural language processing,
and the expansion of knowledge-based approaches to novel domains, such as
Chinese Traditional Medicine and biodiversity.

negative:
Ontology matching is the process of automatically determining the semantic
equivalences between the concepts of two ontologies. Most ontology matching
algorithms are based on two types of strategies: terminology-based strategies,
which align concepts based on their names or descriptions, and structure-based
strategies, which exploit concept hierarchies to find the alignment. In many
domains, there is additional information about the relationships of concepts
represented in various ways, such as Bayesian networks, decision trees, and
association rules. We propose to use the similarities between these
relationships to find more accurate alignments. We accomplish this by defining
soft constraints that prefer alignments where corresponding concepts have the
same local relationships encoded as knowledge rules. We use a probabilistic
framework to integrate this new knowledge-based strategy with standard
terminology-based and structure-based strategies. Furthermore, our method is
particularly effective in identifying correspondences between complex concepts.
Our method achieves substantially better F-score than the previous
state-of-the-art on three ontology matching domains.

negative:
In statistical relational learning, knowledge graph completion deals with
automatically understanding the structure of large knowledge graphs---labeled
directed graphs---and predicting missing relationships---labeled edges.
State-of-the-art embedding models propose different trade-offs between modeling
expressiveness, and time and space complexity. We reconcile both expressiveness
and complexity through the use of complex-valued embeddings and explore the
link between such complex-valued embeddings and unitary diagonalization. We
corroborate our approach theoretically and show that all real square
matrices---thus all possible relation/adjacency matrices---are the real part of
some unitarily diagonalizable matrix. This results opens the door to a lot of
other applications of square matrices factorization. Our approach based on
complex embeddings is arguably simple, as it only involves a Hermitian dot
product, the complex counterpart of the standard dot product between real
vectors, whereas other methods resort to more and more complicated composition
functions to increase their expressiveness. The proposed complex embeddings are
scalable to large data sets as it remains linear in both space and time, while
consistently outperforming alternative approaches on standard link prediction
benchmarks.

negative:
Automatic question-answering is a classical problem in natural language
processing, which aims at designing systems that can automatically answer a
question, in the same way as human does. In this work, we propose a deep
learning based model for automatic question-answering. First the questions and
answers are embedded using neural probabilistic modeling. Then a deep
similarity neural network is trained to find the similarity score of a pair of
answer and question. Then for each question, the best answer is found as the
one with the highest similarity score. We first train this model on a
large-scale public question-answering database, and then fine-tune it to
transfer to the customer-care chat data. We have also tested our framework on a
public question-answering database and achieved very good performance.

negative:
Deep reading models for question-answering have demonstrated promising
performance over the last couple of years. However current systems tend to
learn how to cleverly extract a span of the source document, based on its
similarity with the question, instead of seeking for the appropriate answer.
Indeed, a reading machine should be able to detect relevant passages in a
document regarding a question, but more importantly, it should be able to
reason over the important pieces of the document in order to produce an answer
when it is required. To motivate this purpose, we present ReviewQA, a
question-answering dataset based on hotel reviews. The questions of this
dataset are linked to a set of relational understanding competencies that we
expect a model to master. Indeed, each question comes with an associated type
that characterizes the required competency. With this framework, it is possible
to benchmark the main families of models and to get an overview of what are the
strengths and the weaknesses of a given model on the set of tasks evaluated in
this dataset. Our corpus contains more than 500.000 questions in natural
language over 100.000 hotel reviews. Our setup is projective, the answer of a
question does not need to be extracted from a document, like in most of the
recent datasets, but selected among a set of candidates that contains all the
possible answers to the questions of the dataset. Finally, we present several
baselines over this dataset.

negative:
Question Answering has recently received high attention from artificial
intelligence communities due to the advancements in learning technologies.
Early question answering models used rule-based approaches and moved to the
statistical approach to address the vastly available information. However,
statistical approaches are shown to underperform in handling the dynamic nature
and the variation of language. Therefore, learning models have shown the
capability of handling the dynamic nature and variations in language. Many deep
learning methods have been introduced to question answering. Most of the deep
learning approaches have shown to achieve higher results compared to machine
learning and statistical methods. The dynamic nature of language has profited
from the nonlinear learning in deep learning. This has created prominent
success and a spike in work on question answering. This paper discusses the
successes and challenges in question answering question answering systems and
techniques that are used in these challenges.

negative:
In most current applications of belief networks, domain knowledge is
represented by a single belief network that applies to all problem instances in
the domain. In more complex domains, problem-specific models must be
constructed from a knowledge base encoding probabilistic relationships in the
domain. Most work in knowledge-based model construction takes the rule as the
basic unit of knowledge. We present a knowledge representation framework that
permits the knowledge base designer to specify knowledge in larger semantically
meaningful units which we call network fragments. Our framework provides for
representation of asymmetric independence and canonical intercausal
interaction. We discuss the combination of network fragments to form
problem-specific models to reason about particular problem instances. The
framework is illustrated using examples from the domain of military situation
awareness.

negative:
Question answering is one of the primary challenges of natural language
understanding. In realizing such a system, providing complex long answers to
questions is a challenging task as opposed to factoid answering as the former
needs context disambiguation. The different methods explored in the literature
can be broadly classified into three categories namely: 1) classification
based, 2) knowledge graph based and 3) retrieval based. Individually, none of
them address the need of an enterprise wide assistance system for an IT support
and maintenance domain. In this domain the variance of answers is large ranging
from factoid to structured operating procedures; the knowledge is present
across heterogeneous data sources like application specific documentation,
ticket management systems and any single technique for a general purpose
assistance is unable to scale for such a landscape. To address this, we have
built a cognitive platform with capabilities adopted for this domain. Further,
we have built a general purpose question answering system leveraging the
platform that can be instantiated for multiple products, technologies in the
support domain. The system uses a novel hybrid answering model that
orchestrates across a deep learning classifier, a knowledge graph based context
disambiguation module and a sophisticated bag-of-words search system. This
orchestration performs context switching for a provided question and also does
a smooth hand-off of the question to a human expert if none of the automated
techniques can provide a confident answer. This system has been deployed across
675 internal enterprise IT support and maintenance projects.

negative:
An enhanced approach for network monitoring is to create a network monitoring
tool that has artificial intelligence characteristics. There are a number of
approaches available. One such approach is by the use of a combination of rule
based, fuzzy logic and neural networks to create a hybrid ANFIS system. Such
system will have a dual knowledge database approach. One containing membership
function values to compare to and do deductive reasoning and another database
with rules deductively formulated by an expert (a network administrator). The
knowledge database will be updated continuously with newly acquired patterns.
In short, the system will be composed of 2 parts, learning from data sets and
fine-tuning the knowledge-base using neural network and the use of fuzzy logic
in making decision based on the rules and membership functions inside the
knowledge base. This paper will discuss the idea, steps and issues involved in
creating such a system.

negative:
In this document I present an approach to answer validation and reranking for
question answering (QA) systems. A cased-based reasoning (CBR) system judges
answer candidates for questions from annotated answer candidates for earlier
questions. The promise of this approach is that user feedback will result in
improved answers of the QA system, due to the growing case base. In the paper,
I present the adequate structuring of the case base and the appropriate
selection of relevant similarity measures, in order to solve the answer
validation problem. The structural case base is built from annotated MultiNet
graphs, which provide representations for natural language expressions, and
corresponding graph similarity measures. I cover a priori relations to
experienced answer candidates for former questions. I compare the CBR System
results to current approaches in an experiment integrating CBR into an existing
framework for answer validation and reranking. This integration is achieved by
adding CBR-related features to the input of a learned ranking model that
determines the final answer ranking. In the experiments based on QA@CLEF
questions, the best learned models make heavy use of CBR features. Observing
the results with a continually growing case base, I present a positive effect
of the size of the case base on the accuracy of the CBR subsystem.

negative:
This article describes a novel approach to expand in run-time the knowledge
base of an Artificial Conversational Agent. A technique for automatic knowledge
extraction from the user's sentence and four methods to insert the new acquired
concepts in the knowledge base have been developed and integrated into a system
that has already been tested for knowledge-based conversation between a social
humanoid robot and residents of care homes. The run-time addition of new
knowledge allows overcoming some limitations that affect most robots and
chatbots: the incapability of engaging the user for a long time due to the
restricted number of conversation topics. The insertion in the knowledge base
of new concepts recognized in the user's sentence is expected to result in a
wider range of topics that can be covered during an interaction, making the
conversation less repetitive. Two experiments are presented to assess the
performance of the knowledge extraction technique, and the efficiency of the
developed insertion methods when adding several concepts in the Ontology.

negative:
Since large knowledge bases are typically incomplete, missing facts need to
be inferred from observed facts in a task called knowledge base completion. The
most successful approaches to this task have typically explored explicit paths
through sequences of triples. These approaches have usually resorted to
human-designed sampling procedures, since large knowledge graphs produce
prohibitively large numbers of possible paths, most of which are uninformative.
As an alternative approach, we propose performing a single, short sequence of
interactive lookup operations on an embedded knowledge graph which has been
trained through end-to-end backpropagation to be an optimized and compressed
version of the initial knowledge base. Our proposed model, called Embedded
Knowledge Graph Network (EKGN), achieves new state-of-the-art results on
popular knowledge base completion benchmarks.

negative:
Question Answering (QA) systems are becoming the inspiring model for the
future of search engines. While recently, underlying datasets for QA systems
have been promoted from unstructured datasets to structured datasets with
highly semantic-enriched metadata, but still question answering systems involve
serious challenges which cause to be far beyond desired expectations. In this
paper, we raise the challenges for building a Question Answering (QA) system
especially with the focus of employing structured data (i.e. knowledge graph).
This paper provide an exhaustive insight of the known challenges, so far. Thus,
it helps researchers to easily spot open rooms for the future research agenda.

negative:
People have information needs of varying complexity, which can be solved by
an intelligent agent able to answer questions formulated in a proper way,
eventually considering user context and preferences. In a scenario in which the
user profile can be considered as a question, intelligent agents able to answer
questions can be used to find the most relevant answers for a given user. In
this work we propose a novel model based on Artificial Neural Networks to
answer questions with multiple answers by exploiting multiple facts retrieved
from a knowledge base. The model is evaluated on the factoid Question Answering
and top-n recommendation tasks of the bAbI Movie Dialog dataset. After
assessing the performance of the model on both tasks, we try to define the
long-term goal of a conversational recommender system able to interact using
natural language and to support users in their information seeking processes in
a personalized way.

negative:
This paper presents a system which learns to answer questions on a broad
range of topics from a knowledge base using few hand-crafted features. Our
model learns low-dimensional embeddings of words and knowledge base
constituents; these representations are used to score natural language
questions against candidate answers. Training our system using pairs of
questions and structured representations of their answers, and pairs of
question paraphrases, yields competitive results on a competitive benchmark of
the literature.

negative:
The aim of medical knowledge representation is to capture the detailed domain
knowledge in a clinically efficient manner and to offer a reliable resolution
with the acquired knowledge. The knowledge base to be used by a medical expert
system should allow incremental growth with inclusion of updated knowledge over
the time. As knowledge are gathered from a variety of knowledge sources by
different knowledge engineers, the problem of redundancy is an important
concern here due to increased processing time of knowledge and occupancy of
large computational storage to accommodate all the gathered knowledge. Also
there may exist many inconsistent knowledge in the knowledge base. In this
paper, we have proposed a rough set based lattice structure for knowledge
representation in medical expert systems which overcomes the problem of
redundancy and inconsistency in knowledge and offers computational efficiency
with respect to both time and space. We have also generated an optimal set of
decision rules that would be used directly by the inference engine. The
reliability of each rule has been measured using a new metric called
credibility factor, and the certainty and coverage factors of a decision rule
have been re-defined. With a set of decisions rules arranged in descending
order according to their reliability measures, the medical expert system will
consider the highly reliable and certain rules at first, then it would search
for the possible and uncertain rules at later stage, if recommended by
physicians. The proposed knowledge representation technique has been
illustrated using an example from the domain of low back pain. The proposed
scheme ensures completeness, consistency, integrity, non-redundancy, and ease
of access.

negative:
Search engines can quickly response a hyperlink list according to query
keywords. However, when a query is complex, developers need to repeatedly
refine the search keywords and open a large number of web pages to find and
summarize answers. Many research works of question and answering (Q and A)
system attempt to assist search engines by providing simple, accurate and
understandable answers. However, without original semantic contexts, these
answers lack explainability, making them difficult for users to trust and
adopt. In this paper, a brain-inspired search engine assistant named
DeveloperBot based on knowledge graph is proposed, which aligns to the
cognitive process of human and has the capacity to answer complex queries with
explainability. Specifically, DeveloperBot firstly constructs a multi-layer
query graph by splitting a complex multi-constraint query into several ordered
constraints. Then it models the constraint reasoning process as subgraph search
process inspired by the spreading activation model of cognitive science. In the
end, novel features of the subgraph will be extracted for decision-making. The
corresponding reasoning subgraph and answer confidence will be derived as
explanations. The results of the decision-making demonstrate that DeveloperBot
can estimate the answers and answer confidences with high accuracy. We
implement a prototype and conduct a user study to evaluate whether and how the
direct answers and the explanations provided by DeveloperBot can assist
developers' information needs.

negative:
Nowadays, a huge amount of knowledge has been amassed in digital agriculture.
This knowledge and know-how information are collected from various sources,
hence the question is how to organise this knowledge so that it can be
efficiently exploited. Although this knowledge about agriculture practices can
be represented using ontology, rule-based expert systems, or knowledge model
built from data mining processes, the scalability still remains an open issue.
In this study, we propose a knowledge representation model, called an
ontology-based knowledge map, which can collect knowledge from different
sources, store it, and exploit either directly by stakeholders or as an input
to the knowledge discovery process (Data Mining). The proposed model consists
of two stages, 1) build an ontology as a knowledge base for a specific domain
and data mining concepts, and 2) build the ontology-based knowledge map model
for representing and storing the knowledge mined on the crop datasets. A
framework of the proposed model has been implemented in agriculture domain. It
is an efficient and scalable model, and it can be used as knowledge repository
a digital agriculture.

negative:
Functional and inclusion dependencies are the most widely used classes of
data dependencies in data profiling due to their ability to identify
relationships in data such as primary and foreign keys. These relationships are
equally important when dealing with nested data formats such as JSON. However,
the definition of functional and inclusion dependencies makes use of a flat,
unnested relational model which misses many useful types of dependencies on
data which involve nested data models.
  In this work, we identify types of dependencies which are not captured by
traditional functional and inclusion dependencies but which nevertheless
capture meaningful relationships among nested data. We also demonstrate how
algorithms for mining these traditional dependencies can be adapted to also
mine nested dependencies. The first strategy simply flattens the input data and
feeds into unmodified existing algorithms. We present a second strategy which
instead adapts the algorithm to efficiently process JSON data as input which in
some cases leads to a reduction in runtime by multiple orders of magnitude on
real-world datasets. We further show how these algorithms can be adapted to
produce useful results in the presence of a percentage of incomplete or invalid
data.

negative:
We present a mechanism for constructing graphical models, specifically
Bayesian networks, from a knowledge base of general probabilistic information.
The unique feature of our approach is that it uses a powerful first-order
probabilistic logic for expressing the general knowledge base. This logic
allows for the representation of a wide range of logical and probabilistic
information. The model construction procedure we propose uses notions from
direct inference to identify pieces of local statistical information from the
knowledge base that are most appropriate to the particular event we want to
reason about. These pieces are composed to generate a joint probability
distribution specified as a Bayesian network. Although there are fundamental
difficulties in dealing with fully general knowledge, our procedure is
practical for quite rich knowledge bases and it supports the construction of a
far wider range of networks than allowed for by current template technology.

negative:
Knowledge base is the way to store structured and unstructured data
throughout the web. Since the size of the web is increasing rapidly, there are
huge needs to structure the knowledge in a fully automated way. However
fully-automated knowledge-base evolution on the Semantic Web is a major
challenges, although there are many ontology evolution techniques available.
Therefore learning ontology automatically can contribute to the semantic web
society significantly. In this paper, we propose full-automated ontology
learning algorithm to generate refined knowledge base from incomplete knowledge
base and rdf-triples. Our algorithm is data-driven approach which is based on
the property of each instance. Ontology class is being elaborated by
generalizing frequent property of its instances. By using that developed class
information, each instance can find its most relatively matching class. By
repeating these two steps, we achieve fully-automated ontology evolution from
incomplete basic knowledge base.

negative:
In this paper we discuss the relationships between conditional and
preferential logics and neural network models, based on a multi-preferential
semantics. We propose a concept-wise multipreference semantics, recently
introduced for defeasible description logics to take into account preferences
with respect to different concepts, as a tool for providing a semantic
interpretation to neural network models. This approach has been explored both
for unsupervised neural network models (Self-Organising Maps) and for
supervised ones (Multilayer Perceptrons), and we expect that the same approach
might be extended to other neural network models. It allows for logical
properties of the network to be checked (by model checking) over an
interpretation capturing the input-output behavior of the network. For
Multilayer Perceptrons, the deep network itself can be regarded as a
conditional knowledge base, in which synaptic connections correspond to
weighted conditionals. The paper describes the general approach, through the
cases of Self-Organising Maps and Multilayer Perceptrons, and discusses some
open issues and perspectives.

negative:
Time-efficient link discovery is of central importance to implement the
vision of the Semantic Web. Some of the most rapid Link Discovery approaches
rely internally on planning to execute link specifications. In newer works,
linear models have been used to estimate the runtime the fastest planners.
However, no other category of models has been studied for this purpose so far.
In this paper, we study non-linear runtime estimation functions for runtime
estimation. In particular, we study exponential and mixed models for the
estimation of the runtimes of planners. To this end, we evaluate three
different models for runtime on six datasets using 400 link specifications. We
show that exponential and mixed models achieve better fits when trained but are
only to be preferred in some cases. Our evaluation also shows that the use of
better runtime approximation models has a positive impact on the overall
execution of link specifications.

negative:
Knowledge base construction (KBC) is the process of populating a knowledge
base, i.e., a relational database together with inference rules, with
information extracted from documents and structured sources. KBC blurs the
distinction between two traditional database problems, information extraction
and information integration. For the last several years, our group has been
building knowledge bases with scientific collaborators. Using our approach, we
have built knowledge bases that have comparable and sometimes better quality
than those constructed by human volunteers. In contrast to these knowledge
bases, which took experts a decade or more human years to construct, many of
our projects are constructed by a single graduate student.
  Our approach to KBC is based on joint probabilistic inference and learning,
but we do not see inference as either a panacea or a magic bullet: inference is
a tool that allows us to be systematic in how we construct, debug, and improve
the quality of such systems. In addition, inference allows us to construct
these systems in a more loosely coupled way than traditional approaches. To
support this idea, we have built the DeepDive system, which has the design goal
of letting the user "think about features---not algorithms." We think of
DeepDive as declarative in that one specifies what they want but not how to get
it. We describe our approach with a focus on feature engineering, which we
argue is an understudied problem relative to its importance to end-to-end
quality.

negative:
We introduce an NLP toolkit based on object-oriented knowledge base and
multi-level grammar base. This toolkit focuses on semantic parsing, it also has
abilities to discover new knowledge and grammar automatically, new discovered
knowledge and grammar will be identified by human, and will be used to update
the knowledge base and grammar base. This process can be iterated many times to
improve the toolkit continuously.

negative:
We propose a novel model to address the task of Visual Dialog which exhibits
complex dialog structures. To obtain a reasonable answer based on the current
question and the dialog history, the underlying semantic dependencies between
dialog entities are essential. In this paper, we explicitly formalize this task
as inference in a graphical model with partially observed nodes and unknown
graph structures (relations in dialog). The given dialog entities are viewed as
the observed nodes. The answer to a given question is represented by a node
with missing value. We first introduce an Expectation Maximization algorithm to
infer both the underlying dialog structures and the missing node values
(desired answers). Based on this, we proceed to propose a differentiable graph
neural network (GNN) solution that approximates this process. Experiment
results on the VisDial and VisDial-Q datasets show that our model outperforms
comparative methods. It is also observed that our method can infer the
underlying dialog structure for better dialog reasoning.

negative:
Extracting structured knowledge from texts has traditionally been used for
knowledge base generation. However, other sources of information, such as
images can be leveraged into this process to build more complete and richer
knowledge bases. Structured semantic representation of the content of an image
and knowledge graph embeddings can provide a unique representation of semantic
relationships between image entities. Linking known entities in knowledge
graphs and learning open-world images using language models has attracted lots
of interest over the years. In this paper, we propose a shared learning
approach to learn semantic attributes of images by combining a knowledge graph
embedding model with the recognized attributes of images. The proposed model
premises to help us understand the semantic relationship between the entities
of an image and implicitly provide a link for the extracted entities through a
knowledge graph embedding model. Under the limitation of using a custom
user-defined knowledge base with limited data, the proposed model presents
significant accuracy and provides a new alternative to the earlier approaches.
The proposed approach is a step towards bridging the gap between frameworks
which learn from large amounts of data and frameworks which use a limited set
of predicates to infer new knowledge.

negative:
One of the most difficult problems in the development of intelligent systems
is the construction of the underlying knowledge base. As a consequence, the
rate of progress in the development of this type of system is directly related
to the speed with which knowledge bases can be assembled, and on its quality.
We attempt to solve the knowledge acquisition problem, for a Business
Information System, developing a supervised multistrategy learning paradigm.
This paradigm is centred on a collaborative data mining strategy, where groups
of experts collaborate using data-mining process on the supervised acquisition
of new knowledge extracted from heterogeneous machine learning data models.
  The Actias system is our approach to this paradigm. It is the result of
applying the graphic logic based language of sketches to knowledge integration.
The system is a data mining collaborative workplace, where the Information
System knowledge base is an algebraic structure. It results from the
integration of background knowledge with new insights extracted from data
models, generated for specific data modelling tasks, and represented as rules
using the sketches language.

negative:
With the development of community based question answering (Q&A) services, a
large scale of Q&A archives have been accumulated and are an important
information and knowledge resource on the web. Question and answer matching has
been attached much importance to for its ability to reuse knowledge stored in
these systems: it can be useful in enhancing user experience with recurrent
questions. In this paper, we try to improve the matching accuracy by overcoming
the lexical gap between question and answer pairs. A Word Embedding based
Correlation (WEC) model is proposed by integrating advantages of both the
translation model and word embedding, given a random pair of words, WEC can
score their co-occurrence probability in Q&A pairs and it can also leverage the
continuity and smoothness of continuous space word representation to deal with
new pairs of words that are rare in the training parallel text. An experimental
study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new
method's promising potential.

negative:
With the rapid development of knowledge base,question answering based on
knowledge base has been a hot research issue. In this paper, we focus on
answering singlerelation factoid questions based on knowledge base. We build a
question answering system and study the effect of context information on fact
selection, such as entity's notable type,outdegree. Experimental results show
that context information can improve the result of simple question answering.

negative:
The knowledge engineering bottleneck is still a major challenge in
configurator projects. In this paper we show how recommender systems can
support knowledge base development and maintenance processes. We discuss a
couple of scenarios for the application of recommender systems in knowledge
engineering and report the results of empirical studies which show the
importance of user-centered configuration knowledge organization.

negative:
In many scenarios, complete and incomplete information coexist. For this
reason, the knowledge representation and database communities have long shown
interest in simultaneously supporting the closed- and the open-world views when
reasoning about logic theories. Here we consider the setting of querying
possibly incomplete data using logic theories, formalized as the evaluation of
an ontology-mediated query (OMQ) that pairs a query with a theory, sometimes
called an ontology, expressing background knowledge. This can be further
enriched by specifying a set of closed predicates from the theory that are to
be interpreted under the closed-world assumption, while the rest are
interpreted with the open-world view. In this way we can retrieve more precise
answers to queries by leveraging the partial completeness of the data.
  The central goal of this paper is to understand the relative expressiveness
of OMQ languages in which the ontology is written in the expressive Description
Logic (DL) ALCHOI and includes a set of closed predicates. We consider a
restricted class of conjunctive queries. Our main result is to show that every
query in this non-monotonic query language can be translated in polynomial time
into Datalog with negation under the stable model semantics. To overcome the
challenge that Datalog has no direct means to express the existential
quantification present in ALCHOI, we define a two-player game that
characterizes the satisfaction of the ontology, and design a Datalog query that
can decide the existence of a winning strategy for the game. If there are no
closed predicates, that is in the case of querying a plain ALCHOI knowledge
base, our translation yields a positive disjunctive Datalog program of
polynomial size. To the best of our knowledge, unlike previous translations for
related fragments with expressive (non-Horn) DLs, these are the first
polynomial time translations.

negative:
The dynamics of belief and knowledge is one of the major components of any
autonomous system that should be able to incorporate new pieces of information.
In order to apply the rationality result of belief dynamics theory to various
practical problems, it should be generalized in two respects: first it should
allow a certain part of belief to be declared as immutable; and second, the
belief state need not be deductively closed. Such a generalization of belief
dynamics, referred to as base dynamics, is presented in this paper, along with
the concept of a generalized revision algorithm for knowledge bases (Horn or
Horn logic with stratified negation). We show that knowledge base dynamics has
an interesting connection with kernel change via hitting set and abduction. In
this paper, we show how techniques from disjunctive logic programming can be
used for efficient (deductive) database updates. The key idea is to transform
the given database together with the update request into a disjunctive
(datalog) logic program and apply disjunctive techniques (such as minimal model
reasoning) to solve the original update problem. The approach extends and
integrates standard techniques for efficient query answering and integrity
checking. The generation of a hitting set is carried out through a hyper
tableaux calculus and magic set that is focused on the goal of minimality.

negative:
The aim of this research is to develop a reasoning under uncertainty strategy
in the context of the Fuzzy Inductive Reasoning (FIR) methodology. FIR emerged
from the General Systems Problem Solving developed by G. Klir. It is a data
driven methodology based on systems behavior rather than on structural
knowledge. It is a very useful tool for both the modeling and the prediction of
those systems for which no previous structural knowledge is available. FIR
reasoning is based on pattern rules synthesized from the available data. The
size of the pattern rule base can be very large making the prediction process
quite difficult. In order to reduce the size of the pattern rule base, it is
possible to automatically extract classical Sugeno fuzzy rules starting from
the set of pattern rules. The Sugeno rule base preserves pattern rules
knowledge as much as possible. In this process some information is lost but
robustness is considerably increased. In the forecasting process either the
pattern rule base or the Sugeno fuzzy rule base can be used. The first option
is desirable when the computational resources make it possible to deal with the
overall pattern rule base or when the extracted fuzzy rules are not accurate
enough due to uncertainty associated to the original data. In the second
option, the prediction process is done by means of the classical Sugeno
inference system. If the amount of uncertainty associated to the data is small,
the predictions obtained using the Sugeno fuzzy rule base will be very
accurate. In this paper a mixed pattern/fuzzy rules strategy is proposed to
deal with uncertainty in such a way that the best of both perspectives is used.
Areas in the data space with a higher level of uncertainty are identified by
means of the so-called error models. The prediction process in these areas
makes use of a mixed pattern/fuzzy rules scheme, whereas areas identified with
a lower level of uncertainty only use the Sugeno fuzzy rule base. The proposed
strategy is applied to a real biomedical system, i.e., the central nervous
system control of the cardiovascular system.

negative:
In this paper, we investigate knowledge reasoning within a simple framework
called knowledge structure. We use variable forgetting as a basic operation for
one agent to reason about its own or other agents\ knowledge. In our framework,
two notions namely agents\ observable variables and the weakest sufficient
condition play important roles in knowledge reasoning. Given a background
knowledge base and a set of observable variables for each agent, we show that
the notion of an agent knowing a formula can be defined as a weakest sufficient
condition of the formula under background knowledge base. Moreover, we show how
to capture the notion of common knowledge by using a generalized notion of
weakest sufficient condition. Also, we show that public announcement operator
can be conveniently dealt with via our notion of knowledge structure. Further,
we explore the computational complexity of the problem whether an epistemic
formula is realized in a knowledge structure. In the general case, this problem
is PSPACE-hard; however, for some interesting subcases, it can be reduced to
co-NP. Finally, we discuss possible applications of our framework in some
interesting domains such as the automated analysis of the well-known muddy
children puzzle and the verification of the revised Needham-Schroeder protocol.
We believe that there are many scenarios where the natural presentation of the
available information about knowledge is under the form of a knowledge
structure. What makes it valuable compared with the corresponding multi-agent
S5 Kripke structure is that it can be much more succinct.

negative:
We present a method called TIMEMACHINE to generate a timeline of events and
relations for entities in a knowledge base. For example for an actor, such a
timeline should show the most important professional and personal milestones
and relationships such as works, awards, collaborations, and family
relationships. We develop three orthogonal timeline quality criteria that an
ideal timeline should satisfy: (1) it shows events that are relevant to the
entity; (2) it shows events that are temporally diverse, so they distribute
along the time axis, avoiding visual crowding and allowing for easy user
interaction, such as zooming in and out; and (3) it shows events that are
content diverse, so they contain many different types of events (e.g., for an
actor, it should show movies and marriages and awards, not just movies). We
present an algorithm to generate such timelines for a given time period and
screen size, based on submodular optimization and web-co-occurrence statistics
with provable performance guarantees. A series of user studies using Mechanical
Turk shows that all three quality criteria are crucial to produce quality
timelines and that our algorithm significantly outperforms various baseline and
state-of-the-art methods.

negative:
Ontology-Mediated Query Answering (OMQA) is a well-established framework to
answer queries over an RDFS or OWL Knowledge Base (KB). OMQA was originally
designed for unions of conjunctive queries (UCQs), and based on certain
answers. More recently, OMQA has been extended to SPARQL queries, but to our
knowledge, none of the efforts made in this direction (either in the
literature, or the so-called SPARQL entailment regimes) is able to capture both
certain answers for UCQs and the standard interpretation of SPARQL over a plain
graph. We formalize these as requirements to be met by any semantics aiming at
conciliating certain answers and SPARQL answers, and define three additional
requirements, which generalize to KBs some basic properties of SPARQL answers.
Then we show that a semantics can be defined that satisfies all requirements
for SPARQL queries with SELECT, UNION, and OPTIONAL, and for DLs with the
canonical model property. We also investigate combined complexity for query
answering under such a semantics over DL-Lite R KBs. In particular, we show for
different fragments of SPARQL that known upper-bounds for query answering over
a plain graph are matched.

negative:
Normative expert systems have not become commonplace because they have been
difficult to build and use. Over the past decade, however, researchers have
developed the influence diagram, a graphical representation of a decision
maker's beliefs, alternatives, and preferences that serves as the knowledge
base of a normative expert system. Most people who have seen the representation
find it intuitive and easy to use. Consequently, the influence diagram has
overcome significantly the barriers to constructing normative expert systems.
Nevertheless, building influence diagrams is not practical for extremely large
and complex domains. In this book, I address the difficulties associated with
the construction of the probabilistic portion of an influence diagram, called a
knowledge map, belief network, or Bayesian network. I introduce two
representations that facilitate the generation of large knowledge maps. In
particular, I introduce the similarity network, a tool for building the network
structure of a knowledge map, and the partition, a tool for assessing the
probabilities associated with a knowledge map. I then use these representations
to build Pathfinder, a large normative expert system for the diagnosis of
lymph-node diseases (the domain contains over 60 diseases and over 100 disease
findings). In an early version of the system, I encoded the knowledge of the
expert using an erroneous assumption that all disease findings were
independent, given each disease. When the expert and I attempted to build a
more accurate knowledge map for the domain that would capture the dependencies
among the disease findings, we failed. Using a similarity network, however, we
built the knowledge-map structure for the entire domain in approximately 40
hours. Furthermore, the partition representation reduced the number of
probability assessments required by the expert from 75,000 to 14,000.

negative:
A class or taxonomic hierarchy is often manually constructed, and part of our
knowledge about the world. In this paper, we propose a novel algorithm for
automatically acquiring a class hierarchy from a classifier which is often a
large neural network these days. The information that we need from a classifier
is its confusion matrix which contains, for each pair of base classes, the
number of errors the classifier makes by mistaking one for another. Our
algorithm produces surprisingly good hierarchies for some well-known deep
neural network models trained on the CIFAR-10 dataset, a neural network model
for predicting the native language of a non-native English speaker, a neural
network model for detecting the language of a written text, and a classifier
for identifying music genre. In the literature, such class hierarchies have
been used to provide interpretability to the neural networks. We also discuss
some other potential uses of the acquired hierarchies.

negative:
Globally operating enterprises selling large and complex products and
services often have to deal with situations where variability models are
locally developed to take into account the requirements of local markets. For
example, cars sold on the U.S. market are represented by variability models in
some or many aspects different from European ones. In order to support global
variability management processes, variability models and the underlying
knowledge bases often need to be integrated. This is a challenging task since
an integrated knowledge base should not produce results which are different
from those produced by the individual knowledge bases. In this paper, we
introduce an approach to variability model integration that is based on the
concepts of contextual modeling and conflict detection. We present the
underlying concepts and the results of a corresponding performance analysis.

negative:
This paper develops the concept of knowledge and its exchange using Semantic
Web technologies. It points out that knowledge is more than information because
it embodies the meaning, that is to say semantic and context. These
characteristics will influence our approach to represent and to treat the
knowledge. In order to be adopted, the developed system needs to be simple and
to use standards. The goal of the paper is to find standards to model knowledge
and exchange it with an other person. Therefore, we propose to model knowledge
using UML models to show a graphical representation and to exchange it with XML
to ensure the portability at low cost. We introduce the concept of ontology for
organizing knowledge and for facilitating the knowledge exchange. Proposals
have been tested by implementing an application on the design knowledge of a
pen.

negative:
In this paper we introduce the notion of Demand-Weighted Completeness,
allowing estimation of the completeness of a knowledge base with respect to how
it is used. Defining an entity by its classes, we employ usage data to predict
the distribution over relations for that entity. For example, instances of
person in a knowledge base may require a birth date, name and nationality to be
considered complete. These predicted relation distributions enable detection of
important gaps in the knowledge base, and define the required facts for unseen
entities. Such characterisation of the knowledge base can also quantify how
usage and completeness change over time. We demonstrate a method to measure
Demand-Weighted Completeness, and show that a simple neural network model
performs well at this prediction task.

negative:
Objects in the world usually appear in context, participating in spatial
relationships and interactions that are predictable and expected. Knowledge of
these contexts can be used in the task of using a mobile camera to search for a
specified object in a room. We call this the object search task. This paper is
concerned with representing this knowledge in a manner facilitating its
application to object search while at the same time lending itself to
autonomous learning by a robot. The ability for the robot to learn such
knowledge without supervision is crucial due to the vast number of possible
relationships that can exist for any given set of objects. Moreover, since a
robot will not have an infinite amount of time to learn, it must be able to
determine an order in which to look for possible relationships so as to
maximize the rate at which new knowledge is gained. In effect, there must be a
"focus of interest" operator that allows the robot to choose which examples are
likely to convey the most new information and should be examined first. This
paper demonstrates how a representation based on statistical confidence
intervals allows the construction of a system that achieves the above goals. An
algorithm, based on the Highest Impact First heuristic, is presented as a means
for providing a "focus of interest" with which to control the learning process,
and examples are given.

negative:
This paper describes a process for constructing situation-specific belief
networks from a knowledge base of network fragments. A situation-specific
network is a minimal query complete network constructed from a knowledge base
in response to a query for the probability distribution on a set of target
variables given evidence and context variables. We present definitions of query
completeness and situation-specific networks. We describe conditions on the
knowledge base that guarantee query completeness. The relationship of our work
to earlier work on KBMC is also discussed.

negative:
Ontologies provide a formal description of concepts and their relationships
in a knowledge domain. The goal of ontology alignment is to identify
semantically matching concepts and relationships across independently developed
ontologies that purport to describe the same knowledge. In order to handle the
widest possible class of ontologies, many alignment algorithms rely on
terminological and structural meth- ods, but the often fuzzy nature of concepts
complicates the matching process. However, one area that should provide clear
matching solutions due to its mathematical nature, is units of measurement.
Several on- tologies for units of measurement are available, but there has been
no attempt to align them, notwithstanding the obvious importance for tech-
nical interoperability. We propose a general strategy to map these (and
similar) ontologies by introducing MathML to accurately capture the semantic
description of concepts specified therein. We provide mapping results for three
ontologies, and show that our approach improves on lexical comparisons.

negative:
Existential rules, long known as tuple-generating dependencies in database
theory, have been intensively studied in the last decade as a powerful
formalism to represent ontological knowledge in the context of ontology-based
query answering. A knowledge base is then composed of an instance that contains
incomplete data and a set of existential rules, and answers to queries are
logically entailed from the knowledge base. This brought again to light the
fundamental chase tool, and its different variants that have been proposed in
the literature. It is well-known that the problem of determining, given a chase
variant and a set of existential rules, whether the chase will halt on any
instance, is undecidable. Hence, a crucial issue is whether it becomes
decidable for known subclasses of existential rules. In this work, we consider
linear existential rules, a simple yet important subclass of existential rules
that generalizes inclusion dependencies. We show the decidability of the all
instance chase termination problem on linear rules for three main chase
variants, namely semi-oblivious, restricted and core chase. To obtain these
results, we introduce a novel approach based on so-called derivation trees and
a single notion of forbidden pattern. Besides the theoretical interest of a
unified approach and new proofs, we provide the first positive decidability
results concerning the termination of the restricted chase, proving that chase
termination on linear existential rules is decidable for both versions of the
problem: Does every fair chase sequence terminate? Does some fair chase
sequence terminate?

negative:
Modern knowledge base systems frequently need to combine a collection of
databases in different formats: e.g., relational databases, XML databases, rule
bases, ontologies, etc. In the deductive database system DDBASE, we can manage
these different formats of knowledge and reason about them. Even the file
systems on different computers can be part of the knowledge base. Often, it is
necessary to handle different versions of a knowledge base. E.g., we might want
to find out common parts or differences of two versions of a relational
database.
  We will examine the use of abstractions of rule bases by predicate dependency
and rule predicate graphs. Also the proof trees of derived atoms can help to
compare different versions of a rule base. Moreover, it might be possible to
have derivations joining rules with other formalisms of knowledge
representation.
  Ontologies have shown their benefits in many applications of intelligent
systems, and there have been many proposals for rule languages compatible with
the semantic web stack, e.g., SWRL, the semantic web rule language. Recently,
ontologies are used in hybrid systems for specifying the provenance of the
different components.

negative:
Link prediction in large knowledge graphs has received a lot of attention
recently because of its importance for inferring missing relations and for
completing and improving noisily extracted knowledge graphs. Over the years a
number of machine learning researchers have presented various models for
predicting the presence of missing relations in a knowledge base. Although all
the previous methods are presented with empirical results that show high
performance on select datasets, there is almost no previous work on
understanding the connection between properties of a knowledge base and the
performance of a model. In this paper we analyze the RESCAL method and prove
that it can not encode asymmetric transitive relations in knowledge bases.

negative:
Database schema elements such as tables, views, triggers and functions are
typically defined with many interrelationships. In order to support database
users in understanding a given schema, a rule-based approach for analyzing the
respective dependencies is proposed using Datalog expressions. We show that
many interesting properties of schema elements can be systematically determined
this way. The expressiveness of the proposed analysis is exemplarily shown with
the problem of computing induced functional dependencies for derived relations.
The propagation of functional dependencies plays an important role in data
integration and query optimization but represents an undecidable problem in
general. And yet, our rule-based analysis covers all relational operators as
well as linear recursive expressions in a systematic way showing the depth of
analysis possible by our proposal. The analysis of functional dependencies is
well-integrated in a uniform approach to analyzing dependencies between schema
elements in general.

negative:
Formal verification provides strong safety guarantees but only for models of
cyber-physical systems. Hybrid system models describe the required interplay of
computation and physical dynamics, which is crucial to guarantee what
computations lead to safe physical behavior (e.g., cars should not collide).
Control computations that affect physical dynamics must act in advance to avoid
possibly unsafe future circumstances. Formal verification then ensures that the
controllers correctly identify and provably avoid unsafe future situations
under a certain model of physics. But any model of physics necessarily deviates
from reality and, moreover, any observation with real sensors and manipulation
with real actuators is subject to uncertainty. This makes runtime validation a
crucial step to monitor whether the model assumptions hold for the real system
implementation.
  The key question is what property needs to be runtime-monitored and what a
satisfied runtime monitor entails about the safety of the system: the
observations of a runtime monitor only relate back to the safety of the system
if they are themselves accompanied by a proof of correctness! For an unbroken
chain of correctness guarantees, we, thus, synthesize runtime monitors in a
provably correct way from provably safe hybrid system models. This paper
addresses the inevitable challenge of making the synthesized monitoring
conditions robust to partial observability of sensor uncertainty and partial
controllability due to actuator disturbance. We show that the monitoring
conditions result in provable safety guarantees with fallback controllers that
react to monitor violation at runtime.

negative:
The promise of ultimate elasticity and operational simplicity of serverless
computing has recently lead to an explosion of research in this area. In the
context of data analytics, the concept sounds appealing, but due to the
limitations of current offerings, there is no consensus yet on whether or not
this approach is technically and economically viable. In this paper, we
identify interactive data analytics on cold data as a use case where serverless
computing excels. We design and implement Lambada, a system following a purely
serverless architecture, in order to illustrate when and how serverless
computing should be employed for data analytics. We propose several system
components that overcome the previously known limitations inherent in the
serverless paradigm as well as additional ones we identify in this work. We can
show that, thanks to careful design, a serverless query processing system can
be at the same time one order of magnitude faster and two orders of magnitude
cheaper compared to commercial Query-as-a-Service systems, the only alternative
with similar operational simplicity.

negative:
Clustering is inherently ill-posed: there often exist multiple valid
clusterings of a single dataset, and without any additional information a
clustering system has no way of knowing which clustering it should produce.
This motivates the use of constraints in clustering, as they allow users to
communicate their interests to the clustering system. Active constraint-based
clustering algorithms select the most useful constraints to query, aiming to
produce a good clustering using as few constraints as possible. We propose
COBRA, an active method that first over-clusters the data by running K-means
with a $K$ that is intended to be too large, and subsequently merges the
resulting small clusters into larger ones based on pairwise constraints. In its
merging step, COBRA is able to keep the number of pairwise queries low by
maximally exploiting constraint transitivity and entailment. We experimentally
show that COBRA outperforms the state of the art in terms of clustering quality
and runtime, without requiring the number of clusters in advance.

negative:
The dynamics of belief and knowledge is one of the major components of any
autonomous system that should be able to incorporate new pieces of information.
In this paper, we argue that to apply rationality result of belief dynamics
theory to various practical problems, it should be generalized in two respects:
first of all, it should allow a certain part of belief to be declared as
immutable; and second, the belief state need not be deductively closed. Such a
generalization of belief dynamics, referred to as base dynamics, is presented,
along with the concept of a generalized revision algorithm for Horn knowledge
bases. We show that Horn knowledge base dynamics has interesting connection
with kernel change and abduction. Finally, we also show that both variants are
rational in the sense that they satisfy certain rationality postulates stemming
from philosophical works on belief dynamics.

negative:
We introduce Network Augmentation (NetAug), a new training method for
improving the performance of tiny neural networks. Existing regularization
techniques (e.g., data augmentation, dropout) have shown much success on large
neural networks by adding noise to overcome over-fitting. However, we found
these techniques hurt the performance of tiny neural networks. We argue that
training tiny models are different from large models: rather than augmenting
the data, we should augment the model, since tiny models tend to suffer from
under-fitting rather than over-fitting due to limited capacity. To alleviate
this issue, NetAug augments the network (reverse dropout) instead of inserting
noise into the dataset or the network. It puts the tiny model into larger
models and encourages it to work as a sub-model of larger models to get extra
supervision, in addition to functioning as an independent model. At test time,
only the tiny model is used for inference, incurring zero inference overhead.
We demonstrate the effectiveness of NetAug on image classification and object
detection. NetAug consistently improves the performance of tiny models,
achieving up to 2.2% accuracy improvement on ImageNet. On object detection,
achieving the same level of performance, NetAug requires 41% fewer MACs on
Pascal VOC and 38% fewer MACs on COCO than the baseline.

negative:
Question answering and conversational systems are often baffled and need help
clarifying certain ambiguities. However, limitations of existing datasets
hinder the development of large-scale models capable of generating and
utilising clarification questions. In order to overcome these limitations, we
devise a novel bootstrapping framework (based on self-supervision) that assists
in the creation of a diverse, large-scale dataset of clarification questions
based on post-comment tuples extracted from stackexchange. The framework
utilises a neural network based architecture for classifying clarification
questions. It is a two-step method where the first aims to increase the
precision of the classifier and second aims to increase its recall. We
quantitatively demonstrate the utility of the newly created dataset by applying
it to the downstream task of question-answering. The final dataset, ClarQ,
consists of ~2M examples distributed across 173 domains of stackexchange. We
release this dataset in order to foster research into the field of
clarification question generation with the larger goal of enhancing dialog and
question answering systems.

negative:
In this paper we tackle the problem of routing multiple agents in a
coordinated manner. This is a complex problem that has a wide range of
applications in fleet management to achieve a common goal, such as mapping from
a swarm of robots and ride sharing. Traditional methods are typically not
designed for realistic environments hich contain sparsely connected graphs and
unknown traffic, and are often too slow in runtime to be practical. In
contrast, we propose a graph neural network based model that is able to perform
multi-agent routing based on learned value iteration in a sparsely connected
graph with dynamically changing traffic conditions. Moreover, our learned
communication module enables the agents to coordinate online and adapt to
changes more effectively. We created a simulated environment to mimic realistic
mapping performed by autonomous vehicles with unknown minimum edge coverage and
traffic conditions; our approach significantly outperforms traditional solvers
both in terms of total cost and runtime. We also show that our model trained
with only two agents on graphs with a maximum of 25 nodes can easily generalize
to situations with more agents and/or nodes.

negative:
In this paper we introduce DeepCrawl, a fully-playable Roguelike prototype
for iOS and Android in which all agents are controlled by policy networks
trained using Deep Reinforcement Learning (DRL). Our aim is to understand
whether recent advances in DRL can be used to develop convincing behavioral
models for non-player characters in videogames. We begin with an analysis of
requirements that such an AI system should satisfy in order to be practically
applicable in video game development, and identify the elements of the DRL
model used in the DeepCrawl prototype. The successes and limitations of
DeepCrawl are documented through a series of playability tests performed on the
final game. We believe that the techniques we propose offer insight into
innovative new avenues for the development of behaviors for non-player
characters in video games, as they offer the potential to overcome critical
issues with

negative:
In previous work [BGHK92, BGHK93], we have studied the random-worlds approach
-- a particular (and quite powerful) method for generating degrees of belief
(i.e., subjective probabilities) from a knowledge base consisting of objective
(first-order, statistical, and default) information. But allowing a knowledge
base to contain only objective information is sometimes limiting. We
occasionally wish to include information about degrees of belief in the
knowledge base as well, because there are contexts in which old beliefs
represent important information that should influence new beliefs. In this
paper, we describe three quite general techniques for extending a method that
generates degrees of belief from objective information to one that can make use
of degrees of belief as well. All of our techniques are bloused on well-known
approaches, such as cross-entropy. We discuss general connections between the
techniques and in particular show that, although conceptually and technically
quite different, all of the techniques give the same answer when applied to the
random-worlds method.

negative:
We present a new knowledge-base of hasPart relationships, extracted from a
large corpus of generic statements. Complementary to other resources available,
it is the first which is all three of: accurate (90% precision), salient
(covers relationships a person may mention), and has high coverage of common
terms (approximated as within a 10 year old's vocabulary), as well as having
several times more hasPart entries than in the popular ontologies ConceptNet
and WordNet. In addition, it contains information about quantifiers, argument
modifiers, and links the entities to appropriate concepts in Wikipedia and
WordNet. The knowledge base is available at https://allenai.org/data/haspartkb

negative:
The goal of few-shot learning is to learn a classifier that can recognize
unseen classes from limited support data with labels. A common practice for
this task is to train a model on the base set first and then transfer to novel
classes through fine-tuning (Here fine-tuning procedure is defined as
transferring knowledge from base to novel data, i.e. learning to transfer in
few-shot scenario.) or meta-learning. However, as the base classes have no
overlap to the novel set, simply transferring whole knowledge from base data is
not an optimal solution since some knowledge in the base model may be biased or
even harmful to the novel class. In this paper, we propose to transfer partial
knowledge by freezing or fine-tuning particular layer(s) in the base model.
Specifically, layers will be imposed different learning rates if they are
chosen to be fine-tuned, to control the extent of preserved transferability. To
determine which layers to be recast and what values of learning rates for them,
we introduce an evolutionary search based method that is efficient to
simultaneously locate the target layers and determine their individual learning
rates. We conduct extensive experiments on CUB and mini-ImageNet to demonstrate
the effectiveness of our proposed method. It achieves the state-of-the-art
performance on both meta-learning and non-meta based frameworks. Furthermore,
we extend our method to the conventional pre-training + fine-tuning paradigm
and obtain consistent improvement.

negative:
A Concept Tree is a structure for storing knowledge where the trees are
stored in a database called a Concept Base. It sits between the highly
distributed neural architectures and the distributed information systems, with
the intention of bringing brain-like and computer systems closer together.
Concept Trees can grow from the semi-structured sources when consistent
sequences of concepts are presented. Each tree ideally represents a single
cohesive concept and the trees can link with each other for navigation and
semantic purposes. The trees are therefore also a type of semantic network and
would benefit from having a consistent level of context for each node. A
consistent build process is managed through a 'counting rule' and some other
rules that can normalise the database structure. This restricted structure can
then be complimented and enriched by the more dynamic context. It is also
suggested to use the linking structure of the licas system [15] as a basis for
the context links, where the mathematical model is extended further to define
this. A number of tests have demonstrated the soundness of the architecture.
Building the trees from text documents shows that the tree structure could be
inherent in natural language. Then, two types of query language are described.
Both of these can perform consistent query processes to return knowledge to the
user and even enhance the query with new knowledge. This is supported even
further with direct comparisons to a cognitive model, also being developed by
the author.