Learning from Ordinal Data with Inductive Logic Programming in Description Logic

Nunung Nurul Qomariyah, Dimitar Lubomirov Kazakov

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationLate Breaking Papers of the 27th International Conference on Inductive Logic Programming
EditorsNicolas Lachiche, Christel Vrain
Place of Publicationhttp://ceur-ws.org/Vol-2085/
PublisherCEUR Workshop Proceedings
Number of pages13
ISBN (Electronic)ISSN 1613-0073
Publication statusPublished - 29 Mar 2018
Event27th International Conference on Inductive Logic Programming - Centre International Universitaire pour la Recherche, Orléans, France
Duration: 4 Sept 20176 Sept 2017
Conference number: 27


Conference27th International Conference on Inductive Logic Programming
Abbreviated titleILP '17
Internet address

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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