Top Level Ontology
Top-level ontologies provide a standardized, high-level framework for organizing and representing knowledge across diverse domains, aiming to improve data interoperability and facilitate knowledge sharing. Current research emphasizes using ontologies to enhance explainability in complex systems like multimodal large language models and to improve the efficiency of tasks such as ontology versioning and knowledge graph construction, often leveraging techniques like ontology matching and rule-based reasoning. This work has significant implications for various fields, enabling more robust and reliable AI systems, improved data management in scientific research, and more efficient knowledge discovery across diverse applications.
Papers
From human experts to machines: An LLM supported approach to ontology and knowledge graph construction
Vamsi Krishna Kommineni, Birgitta König-Ries, Sheeba Samuel
GPT, Ontology, and CAABAC: A Tripartite Personalized Access Control Model Anchored by Compliance, Context and Attribute
Raza Nowrozy, Khandakar Ahmed, Hua Wang