Knowledge Tree
ontologies
Facts (43)
Sources
Construction of Knowledge Graphs: State and Challenges - arXiv arxiv.org 9 facts
claimWorldKG and HKGB utilize semi-automatic methods to build initial ontologies, which is more advanced than manual ontology construction.
claimThe Web Ontology Language (OWL), specifically version OWL 2, is a widely used approach for defining ontologies by adding semantics to data using a variety of axioms.
referenceThe article 'An analysis of ontologies and their success factors for application to business' by C. Feilmayr and W. Wöß was published in the journal Data & Knowledge Engineering in 2016.
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).
claimOntologies enable the inference of new implicit knowledge from explicitly represented information in a knowledge graph.
procedureRDFUnit enables the automatic generation of tests by analyzing the structure of schemata, such as ontologies or vocabularies, and allows users to define custom validation rules.
claimPublic web wikis, catalogs, APIs, and crowdsourced databases serve as valuable starting sources for creating initial ontologies because they often contain large amounts of structured or semi-structured data.
referencePortisch et al. use a variation of the RDF2Vec walk-based embedding technique to encode ontologies and a rotation matrix to align the embeddings, leveraging the ability of graph embeddings to capture structural information.
claimProperty Graph Models lack built-in support for ontologies, such as providing 'is-a' relations between entity categories.
Applying Large Language Models in Knowledge Graph-based ... arxiv.org Jan 7, 2025 5 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.
claimOntologies use formal notation and axioms to enable reasoning and inferencing, which allows for the derivation of new knowledge.
claimKnowledge graphs use ontologies as formal knowledge bases to acquire and integrate information, as characterized by Ehrlinger and Wöß.
referenceHöfferer, P. published 'Achieving business process model interoperability using metamodels and ontologies' in the proceedings of the 15th European Conference on Information Systems (ECIS 2007) in 2007.
quoteOntologies are characterized as "a shared and common understanding of some domain that can be communicated across people and computers."
Addressing common challenges with knowledge graphs - SciBite scibite.com 5 facts
claimSciBite's CENtree platform provides a centralized resource for managing, extending, and creating new ontologies for domains not covered by existing vocabularies.
claimSemantic enrichment, or curating unstructured scientific text with ontologies, allows computers to understand data as 'things, not strings' by giving explicit meaning to terms and encapsulating relationships.
procedureTo extract relationships from text, SciBite defines semantic patterns called 'bundles' in the form of Gene-Verb-Drug, which are then processed using the TExpress tool to generate semantic triples aligned to ontologies.
claimSciBite's TERMite Named Entity Recognition (NER) engine identifies scientific entities in unstructured text and aligns them to unique identifiers in ontologies to produce structured data.
claimOntologies provide a common model of knowledge for a domain, such as classifying Type II Diabetes Mellitus as an endocrine disease.
How to combine LLMs and Knowledge Graphs for enterprise AI linkedin.com Nov 14, 2025 3 facts
perspectiveThe author posits that while ontologies and truth-verification systems are both important, only one of these layers is foundational.
claimOntologies assist AI systems in the representation of knowledge.
claimCrebiliti verifies the truth-physics behind information organized by ontologies.
Enterprise AI Requires the Fusion of LLM and Knowledge Graph stardog.com Dec 4, 2024 3 facts
claimStardog uses LLMs to automate the creation of ontologies from plain language prompts, allowing subject-matter experts to act as knowledge engineers without requiring specialized knowledge engineering training.
claimStardog Designer implements semi-supervised automation by queuing modeling suggestions for a human user to accept before any changes are applied to real ontologies.
claimStardog Designer enriches existing ontologies by running asynchronously in the background to offer modeling suggestions, including new subsumption relationships, new node classifications, new predicate types, and disjointedness axioms.
The Year of Neuro-Symbolic AI: How 2026 Makes Machines Actually ... cogentinfo.com Dec 30, 2025 2 facts
procedureOrganizations can create a reusable foundation for AI systems by structuring policies, workflows, taxonomies, and domain expertise into ontologies and knowledge bases.
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.
KG-RAG: Bridging the Gap Between Knowledge and Creativity - arXiv arxiv.org May 20, 2024 2 facts
claimThe authors of the KG-RAG paper propose a flexible, domain-agnostic, homogeneous Knowledge Graph framework to overcome the limitations of rigid, domain-specific ontologies.
claimThe authors of the KG-RAG paper propose a flexible, domain-agnostic, homogeneous Knowledge Graph framework to overcome the limitations of rigid, domain-specific ontologies.
LLM-Powered Knowledge Graphs for Enterprise Intelligence and ... arxiv.org Mar 11, 2025 2 facts
claimThe framework described in the paper utilizes a system-agnostic design that avoids rigid ontologies to maintain scalability and flexibility across diverse enterprise contexts.
claimExisting knowledge graph approaches often depend on rigid ontologies and system-specific implementations, which makes them difficult to scale and adapt to the diverse and dynamic needs of modern enterprises.
The Rise of Neuro-Symbolic AI: A Spotlight in Gartner's 2025 AI ... allegrograph.com Jul 28, 2025 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.
The Synergy of Symbolic and Connectionist AI in LLM ... arxiv.org 1 fact
claimOntologies provide a framework for annotating and interlinking data by serving as formal specifications of concepts and relationships, enabling semantic reasoning.
LLM-empowered knowledge graph construction: A survey - arXiv arxiv.org Oct 23, 2025 1 fact
claimIn the pre-LLM era, ontologies were primarily manually constructed by domain experts, supported by semantic web tools such as Protégé and guided by established methodologies including METHONTOLOGY and On-To-Knowledge.
A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Dec 9, 2025 1 fact
claimA future research direction for neuro-symbolic AI is knowledge base verification, where neural components propose new links or facts, and symbolic components enforce consistency with known facts or ontologies, using uncertainty measures to assess plausibility.
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.
Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org Feb 16, 2025 1 fact
claimMethods such as Graph Neural Networks (GNNs), Named Entity Recognition (NER), link prediction, and relation extraction fall into the Neuro[Symbolic] category because they leverage symbolic relationships like ontologies or graphs to enhance neural processing.
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.
The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org Jul 11, 2024 1 fact
claimThe synergy of Ontologies and Markov-logic networks improved the ability of symbolic AI to perform robust reasoning over large datasets.
Combining Knowledge Graphs and Large Language Models - arXiv arxiv.org Jul 9, 2024 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.