811 research outputs found
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge
graphs that represent structural relations between entities have become an
increasingly popular research direction towards cognition and human-level
intelligence. In this survey, we provide a comprehensive review of knowledge
graph covering overall research topics about 1) knowledge graph representation
learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph,
and 4) knowledge-aware applications, and summarize recent breakthroughs and
perspective directions to facilitate future research. We propose a full-view
categorization and new taxonomies on these topics. Knowledge graph embedding is
organized from four aspects of representation space, scoring function, encoding
models, and auxiliary information. For knowledge acquisition, especially
knowledge graph completion, embedding methods, path inference, and logical rule
reasoning, are reviewed. We further explore several emerging topics, including
meta relational learning, commonsense reasoning, and temporal knowledge graphs.
To facilitate future research on knowledge graphs, we also provide a curated
collection of datasets and open-source libraries on different tasks. In the
end, we have a thorough outlook on several promising research directions
The use of proof plans in tactic synthesis
We undertake a programme of tactic synthesis. We first formalize the notion of
a tactic as a rewrite rule, then give a correctness criterion for this by means of a
reflection mechanism in the constructive type theory OYSTER. We further formalize
the notion of a tactic specification, given as a synthesis goal and a decidability
goal. We use a proof planner. CIAM. to guide the search for inductive proofs
of these, and are able to successfully synthesize several tactics in this fashion.
This involves two extensions to existing methods: context-sensitive rewriting and
higher-order wave rules. Further, we show that from a proof of the decidability
goal one may compile to a Prolog program a pseudo- tactic which may be run to
efficiently simulate the input/output behaviour of the synthetic tacti
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
Investigation, Development, and Evaluation of Performance Proving for Fault-tolerant Computers
A number of methodologies for verifying systems and computer based tools that assist users in verifying their systems were developed. These tools were applied to verify in part the SIFT ultrareliable aircraft computer. Topics covered included: STP theorem prover; design verification of SIFT; high level language code verification; assembly language level verification; numerical algorithm verification; verification of flight control programs; and verification of hardware logic
Ontology Reasoning with Deep Neural Networks
The ability to conduct logical reasoning is a fundamental aspect of
intelligent behavior, and thus an important problem along the way to
human-level artificial intelligence. Traditionally, symbolic logic-based
methods from the field of knowledge representation and reasoning have been used
to equip agents with capabilities that resemble human logical reasoning
qualities. More recently, however, there has been an increasing interest in
using machine learning rather than symbolic logic-based formalisms to tackle
these tasks. In this paper, we employ state-of-the-art methods for training
deep neural networks to devise a novel model that is able to learn how to
effectively perform logical reasoning in the form of basic ontology reasoning.
This is an important and at the same time very natural logical reasoning task,
which is why the presented approach is applicable to a plethora of important
real-world problems. We present the outcomes of several experiments, which show
that our model learned to perform precise ontology reasoning on diverse and
challenging tasks. Furthermore, it turned out that the suggested approach
suffers much less from different obstacles that prohibit logic-based symbolic
reasoning, and, at the same time, is surprisingly plausible from a biological
point of view
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