Publication: Understanding and improving ontology reasoning efficiency through learning and ranking
Issued Date
2020-01-01
Resource Type
ISSN
03064379
Other identifier(s)
2-s2.0-85068877969
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Mahidol University
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SCOPUS
Bibliographic Citation
Information Systems. Vol.87, (2020)
Suggested Citation
Yong Bin Kang, Shonali Krishnaswamy, Wudhichart Sawangphol, Lianli Gao, Yuan Fang Li Understanding and improving ontology reasoning efficiency through learning and ranking. Information Systems. Vol.87, (2020). doi:10.1016/j.is.2019.07.002 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/49589
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Title
Understanding and improving ontology reasoning efficiency through learning and ranking
Abstract
© 2019 Ontologies are the fundamental building blocks of the Semantic Web and Linked Data. Reasoning is critical to ensure the logical consistency of ontologies, and to compute inferred knowledge from an ontology. It has been shown both theoretically and empirically that, despite decades of intensive work on optimising ontology reasoning algorithms, performing core reasoning tasks on large and expressive ontologies is time-consuming and resource-intensive. In this paper, we present the meta-reasoning framework R2O2* to tackle the important problems of understanding the source of TBox reasoning hardness and predicting and optimising TBox reasoning efficiency by exploiting machine learning techniques. R2O2* combines state-of-the-art OWL 2 DL reasoners as well as an efficient OWL 2 EL reasoner as components, and predicts the most efficient one by using an ensemble of robust learning algorithms including XGBoost and Random Forests. A comprehensive evaluation on a large and carefully curated ontology corpus shows that R2O2* outperforms all six component reasoners as well as AutoFolio, a robust and strong algorithm selection system.