Learning from Ordinal Data with Inductive Logic Programming in Description Logic

Nunung Nurul Qomariyah; Dimitar Lubomirov Kazakov

Learning from Ordinal Data with Inductive Logic Programming in Description Logic

Nunung Nurul Qomariyah, Dimitar Lubomirov Kazakov

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Online proceedings of the 27th conference on Inductive Logic Programming
Publication status	Published - Sep 2017

Keywords

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

Access to Document

ILP_2017_paper_29 (1)Accepted author manuscript, 244 KBLicence: Unspecified

Cite this

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title = "Learning from Ordinal Data with Inductive Logic Programming in Description Logic",

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.",

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AU - Qomariyah, Nunung Nurul

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N2 - 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.

AB - 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.

KW - Inductive Logic Programming (ILP)

KW - pairwise comparisons

KW - preference learning

KW - Machine Learning

KW - description logic

M3 - Conference contribution

BT - Online proceedings of the 27th conference on Inductive Logic Programming

ER -