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Characterising semantic relatedness using interpretable directions in conceptual spaces

Derrac, Joaquin and Schockaert, Steven ORCID: 2014. Characterising semantic relatedness using interpretable directions in conceptual spaces. Presented at: 2nd European Conference on Artificial Intelligence (ECAI), Prague, Czech Republic, 18-22 August 2014. Published in: Schaub, Torsten, Friedrich, Gerhard and O'Sullivan, Barry eds. ECAI 2014: 21st European Conference on Artificial Intelligence. Frontiers in Artificial Intelligence and Applications , vol.263 Amsterdam: IOS Press, pp. 243-248. 10.3233/978-1-61499-419-0-243

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Various applications, such as critique-based recommendation systems and analogical classifiers, rely on knowledge of how different entities relate. In this paper, we present a methodology for identifying such semantic relationships, by interpreting them as qualitative spatial relations in a conceptual space. In particular, we use multi-dimensional scaling to induce a conceptual space from a relevant text corpus and then identify directions that correspond to relative properties such as “more violent than” in an entirely unsupervised way. We also show how a variant of FOIL is able to learn natural categories from such qualitative representations, by simulating a fortiori inference, an important pattern of commonsense reasoning.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: IOS Press
ISBN: 9781614994183
Funders: EPSRC
Date of First Compliant Deposit: 30 March 2016
Last Modified: 27 Oct 2022 10:01

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