STRiKE: Rule-driven relational learning using stratified k-entailment

Svatoš, Martin, Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881, Davis, Jesse and Kuželka, Ondrej 2020. STRiKE: Rule-driven relational learning using stratified k-entailment. Presented at: European Conference on Artificial Intelligence (ECAI2020), Santiago de Compostela, Spain, 29 August - 2 September. Published in: De Giacomo, G., Catala, A., Dilkina, B., Milano, M., Barro, S., Bugarin, A. and Lang, J. eds. Proceedings of the 24th European Conference on Artificial Intelligence. , vol.325 IOS Press, pp. 1515-1522. 10.3233/FAIA200259

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Abstract

Relational learning for knowledge base completion has been receiving considerable attention. Intuitively, rule-based strategies are clearly appealing, given their transparency and their ability to capture complex relational dependencies. In practice, however, pure rule-based strategies are currently not competitive with state-of-the-art methods, which is a reflection of the fact that (i) learning high-quality rules is challenging, and (ii) classical entailment is too brittle to cope with the noisy nature of the learned rules and the given knowledge base. In this paper, we introduce STRiKE, a new approach for relational learning in knowledge bases which addresses these concerns. Our contribution is three-fold. First, we introduce a new method for learning stratified rule bases from relational data. Second, to use these rules in a noise-tolerant way, we propose a strategy which extends k-entailment, a recently introduced cautious entailment relation, to stratified rule bases. Finally, we introduce an efficient algorithm for reasoning based on k-entailment.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	IOS Press
Date of First Compliant Deposit:	14 April 2020
Date of Acceptance:	14 January 2020
Last Modified:	24 Sep 2025 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/130911

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