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Learning from Ordinal Data with Inductive Logic Programming in Description Logic

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Title of host publicationLate Breaking Papers of the 27th International Conference on Inductive Logic Programming
DatePublished - 29 Mar 2018
Pages38-50
Number of pages13
PublisherCEUR Workshop Proceedings
Place of Publicationhttp://ceur-ws.org/Vol-2085/
EditorsNicolas Lachiche, Christel Vrain
Volume2085
Original languageEnglish
ISBN (Electronic)ISSN 1613-0073

Abstract

Here we describe a Description Logic (DL) based Inductive Logic Programming (ILP) algorithm for learning relations of order. We test our algorithm on the task of learning user preferences from pairwise comparisons. The results have implications for the development of customised recommender systems for e-commerce, and more broadly, wherever DL-based representations of knowledge, such as OWL ontologies, are used. The use of DL makes for easy integration with such data, and produces hypotheses that are easy to interpret by novice users. The proposed algorithm outperforms SVM, Decision Trees and Aleph on data from two domains.

Bibliographical note

An earlier version of this paper was accepted for publication in 2017 and entered into PURE. This extended version of the paper was subject to another round of reviews, and was published as an open access paper in these online proceedings on 29 March 2018 (see link above in this record).

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