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Automated rule base completion as Bayesian concept induction

Bouraoui, Zied and Schockaert, Steven 2019. Automated rule base completion as Bayesian concept induction. Presented at: AAAI-2019: 33rd AAAI Conference on Artificial Intelligence, Honololu, HI, USA, 27 January - 1 February 2019. Proceedings of the AAAI Conference on Artificial Intelligence. , vol. 1. Palo Alto, California: AAAI Press, pp. 6228-6235. 10.1609/aaai.v33i01.33016228

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Abstract

Considerable attention has recently been devoted to the problem of automatically extending knowledge bases by applying some form of inductive reasoning. While the vast majority of existing work is centred around so-called knowledge graphs, in this paper we consider a setting where the input consists of a set of (existential) rules. To this end, we exploit a vector space representation of the considered concepts, which is partly induced from the rule base itself and partly from a pre-trained word embedding. Inspired by recent approaches to concept induction, we then model rule templates in this vector space embedding using Gaussian distributions. Unlike many existing approaches, we learn rules by directly exploiting regularities in the given rule base, and do not require that a database with concept and relation instances is given. As a result, our method can be applied to a wide variety of ontologies. We present experimental results that demonstrate the effectiveness of our method.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: AAAI Press
ISSN: 2374-3468
Date of First Compliant Deposit: 6 February 2019
Last Modified: 28 Aug 2019 08:41
URI: http://orca.cf.ac.uk/id/eprint/117379

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