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related 11.00 — strongly supporting 11 facts
Knowledge graphs and ontologies are intrinsically linked as they are frequently studied together in research conferences like KR 2026 {fact:2, 3, 5} and serve as complementary components in neuro-symbolic architectures {fact:1, 10}. Ontologies provide the formal structural metadata and semantic schemas necessary for knowledge graphs to function {fact:6, 7, 8, 9}, and they are often constructed together using frameworks like TKGCon [1].
Facts (11)
Sources
Applying Large Language Models in Knowledge Graph-based ... arxiv.org 2 facts
claimOntologies and knowledge graphs are useful for supporting the automated generation of enterprise models because they can formally express semantics and make them machine-processable.
claimKnowledge graphs use ontologies as formal knowledge bases to acquire and integrate information, as characterized by Ehrlinger and Wöß.
The Rise of Neuro-Symbolic AI: A Spotlight in Gartner's 2025 AI ... allegrograph.com 1 fact
claimAllegroGraph, a product of Franz Inc., serves as a knowledge layer in Neuro-Symbolic architectures by providing support for knowledge graphs, ontologies, SHACL constraints, and SPARQL-based inferencing.
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 1 fact
claimKnowledge graphs contain three primary types of metadata: descriptive metadata (content information for discovery), structural metadata (schemas and ontologies), and administrative metadata (technical and process aspects like provenance and mapping specifications).
Overcoming the limitations of Knowledge Graphs for Decision ... xpertrule.com 1 fact
claimKnowledge graphs and their associated ontologies provide a method to surface insights by visualizing complex data relationships as graph structures, facilitating the search and query of interconnected information.
KR 2026 : 23rd International Conference on Principles of ... - WikiCFP wikicfp.com 1 fact
claimThe 23rd International Conference on Principles of Knowledge Representation and Reasoning (KR 2026) covers research topics including argumentation, belief change, common-sense reasoning, computational aspects of knowledge representation, description logics, ethical considerations in knowledge representation, explanation, abduction and diagnosis, geometric, spatial, and temporal reasoning, inconsistency- and exception-tolerant reasoning, knowledge acquisition, knowledge compilation, automated reasoning, satisfiability and model counting, knowledge representation languages, logic programming, answer set programming, model learning for diagnosis and planning, modeling and reasoning about preferences, modeling constraints and constraint solving, multi- and order-sorted representations and reasoning, non-monotonic logics, ontologies and knowledge-enriched data management, philosophical foundations of knowledge representation, qualitative reasoning, reasoning about actions and change, action languages, reasoning about knowledge, beliefs, and other mental attitudes, reasoning in knowledge graphs, reasoning in multi-agent systems, semantic web, similarity-based and contextual reasoning, and uncertainty and vagueness.
Call for Papers: Main Track - KR 2026 kr.org 1 fact
claimThe KR 2026 conference accepts submissions on topics including argumentation, belief change, common-sense reasoning, computational aspects of knowledge representation, description logics, ethical considerations in KR, explanation/abduction/diagnosis, geometric/spatial/temporal reasoning, inconsistency- and exception-tolerant reasoning, knowledge acquisition, knowledge compilation/automated reasoning/satisfiability/model counting, knowledge representation languages, logic programming/answer set programming, model learning for diagnosis and planning, modeling and reasoning about preferences, modeling constraints and constraint solving, multi- and order-sorted representations and reasoning, non-monotonic logics, ontologies and knowledge-enriched data management, philosophical foundations of KR, qualitative reasoning, reasoning about actions and change/action languages, reasoning about knowledge/beliefs/mental attitudes, reasoning in knowledge graphs, reasoning in multi-agent systems, semantic web, similarity-based and contextual reasoning, and uncertainty and vagueness.
Combining Knowledge Graphs and Large Language Models - arXiv arxiv.org 1 fact
referenceTKGCon (Theme-specific Knowledge Graph Construction) is an unsupervised framework that uses Large Language Models to construct ontologies and theme-specific knowledge graphs by generating and deciding relations between entities to create graph edges.
Call for Papers: KR meets Machine Learning and Explanation kr.org 1 fact
claimThe KR 2026 special track 'KR meets Machine Learning and Explanation' invites research on the intersection of Knowledge Representation and Machine Learning, specifically covering topics such as learning symbolic knowledge (ontologies, knowledge graphs, action theories), KR-driven plan computation, logic-based learning, neural-symbolic learning, statistical relational learning, symbolic reinforcement learning, and the mutual use of KR techniques and LLMs.
Call for Papers: Special Session on KR and Machine Learning kr.org 1 fact
claimThe Special Session on KR and Machine Learning at KR2022 welcomes papers on topics including learning symbolic knowledge (ontologies, knowledge graphs, action theories, commonsense knowledge, spatial/temporal theories, preference/causal models), logic-based/relational learning algorithms, machine-learning driven reasoning, neural-symbolic learning, statistical relational learning, multi-agent learning, symbolic reinforcement learning, learning symbolic abstractions from unstructured data, explainable AI, expressive power of learning representations, knowledge-driven natural language understanding and dialogue, knowledge-driven decision making, knowledge-driven intelligent systems for IoT and cybersecurity, and architectures combining data-driven techniques with formal reasoning.
The Year of Neuro-Symbolic AI: How 2026 Makes Machines Actually ... cogentinfo.com 1 fact
referenceThe symbolic knowledge layer of a neuro-symbolic system stores structured intelligence in formats such as ontologies, rule sets, taxonomies, and knowledge graphs, allowing the system to interpret meaning through logical inference mechanisms rather than just pattern recognition.