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Learning conceptual space representations of interrelated concepts

Bouraoui, Zied and Schockaert, Steven 2018. Learning conceptual space representations of interrelated concepts. Presented at: 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI-18), Stockholm, Sweden, 13-19 July 2018. Published in: Lang, Jerome ed. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, pp. 1760-1766. 10.24963/ijcai.2018/243

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Abstract

Several recently proposed methods aim to learn conceptual space representations from large text collections. These learned representations associate each object from a given domain of interest with a point in a high-dimensional Euclidean space, but they do not model the concepts from this domain, and can thus not directly be used for categorization and related cognitive tasks. A natural solution is to represent concepts as Gaussians, learned from the representations of their instances, but this can only be reliably done if sufficiently many instances are given, which is often not the case. In this paper, we introduce a Bayesian model which addresses this problem by constructing informative priors from background knowledge about how the concepts of interest are interrelated with each other. We show that this leads to substantially better predictions in a knowledge base completion task.

Item Type: Conference or Workshop Item (Paper)
Status: Published
Schools: Computer Science & Informatics
Publisher: International Joint Conferences on Artificial Intelligence Organization
ISBN: 9780999241127
Date of First Compliant Deposit: 18 July 2018
Date of Acceptance: 4 May 2018
Last Modified: 12 Jun 2019 03:04
URI: http://orca.cf.ac.uk/id/eprint/112685

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