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 publicationOnline proceedings of the 27th conference on Inductive Logic Programming
Publication statusPublished - Sep 2017


  • Inductive Logic Programming (ILP)
  • pairwise comparisons
  • preference learning
  • Machine Learning
  • description logic

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