Towards meta-reasoning for ontologies: a roadmap

Yuan Fang Li, Yong Bin Kang

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review


Ontologies are widely used to formally represent abstract domain knowledge. Logic reasoning ensures the logical consistency of ontologies, and infers knowledge implicitly encoded in ontologies. It has been shown both theoretically and empirically that for large and complex ontologies, reasoning is still time-consuming and resource-intensive. Meta-reasoning exploits machine learning techniques to tackle the important problems of understanding the source of reasoning hardness and to predict reasoning efficiency, with the overall goal of improving reasoning efficiency. In this paper, we highlight recent advances in meta-reasoning for Semantic Web ontologies, briefly present technical innovations and results, and discuss important problems for future research.

Original languageEnglish
Title of host publicationECAI Digital - 2020
Subtitle of host publication24th European Conference on Artificial Intelligence
EditorsGiuseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senén Barro, Alberto Bugarín, Jérôme Lang
Place of PublicationAmsterdam Netherlands
PublisherIOS Press
Number of pages2
ISBN (Electronic)9781643681016
ISBN (Print)9781643681009
Publication statusPublished - 24 Aug 2020
EventEuropean Conference on Artificial Intelligence 2020 - Virtual, Santiago de Compostela, Spain
Duration: 29 Aug 20208 Sept 2020
Conference number: 24th (Website) (Proceedings)

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389


ConferenceEuropean Conference on Artificial Intelligence 2020
Abbreviated titleECAI 2020
CitySantiago de Compostela
Other24th European Conference on Artificial Intelligence, ECAI 2020, including 10th Conference on Prestigious Applications of Artificial Intelligence, PAIS 2020
Internet address

Cite this