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
Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)
Sabrina Toro, Anna V Anagnostopoulos, Sue Bello, Kai Blumberg, Rhiannon Cameron, Leigh Carmody, Alexander D Diehl, Damion Dooley, William Duncan, Petra Fey, Pascale Gaudet, Nomi L Harris, Marcin Joachimiak, Leila Kiani, Tiago Lubiana, Monica C Munoz-Torres, Shawn O'Neil, David Osumi-Sutherland, Aleix Puig, Justin P Reese, Leonore Reiser, Sofia Robb, Troy Ruemping, James Seager, Eric Sid, Ray Stefancsik, Magalie Weber, Valerie Wood, Melissa A Haendel, Christopher J Mungall
Minimal Macro-Based Rewritings of Formal Languages: Theory and Applications in Ontology Engineering (and beyond)
Christian Kindermann, Anne-Marie George, Bijan Parsia, Uli Sattler