Qomariyah, Nunung Nurul and Kazakov, Dimitar Lubomirov orcid.org/0000-0002-0637-8106 (2018) Learning from Ordinal Data with Inductive Logic Programming in Description Logic. In: Lachiche, Nicolas and Vrain, Christel, (eds.) Late Breaking Papers of the 27th International Conference on Inductive Logic Programming. 27th International Conference on Inductive Logic Programming, 04-06 Sep 2017 CEUR Workshop Proceedings , FRA , pp. 38-50.
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.
Metadata
Item Type: | Proceedings Paper |
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Copyright, Publisher and Additional Information: | 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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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 30 May 2018 11:40 |
Last Modified: | 16 Oct 2024 10:58 |
Status: | Published |
Publisher: | CEUR Workshop Proceedings |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:131278 |
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