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	Late Breaking Papers of the 27th International Conference on Inductive Logic Programming
Editors	Nicolas Lachiche, Christel Vrain
Place of Publication	http://ceur-ws.org/Vol-2085/
Publisher	CEUR Workshop Proceedings
Pages	38-50
Number of pages	13
Volume	2085
ISBN (Electronic)	ISSN 1613-0073
Publication status	Published - 29 Mar 2018
Event	27th International Conference on Inductive Logic Programming - Centre International Universitaire pour la Recherche, Orléans, France Duration: 4 Sep 2017 → 6 Sep 2017 Conference number: 27 https://ilp2017.sciencesconf.org

Conference

Conference	27th International Conference on Inductive Logic Programming
Abbreviated title	ILP '17
Country/Territory	France
City	Orléans
Period	4/09/17 → 6/09/17
Internet address	https://ilp2017.sciencesconf.org

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).

Access to Document

qomariyahLBP-ILP2017
© The Authors, 2017.
Final published version, 621 KBLicence: CC0

http://ceur-ws.org/Vol-2085/Licence: Unspecified

Cite this

@inproceedings{704a80c512d5433d8e3661de67ee4715,

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.",

author = "Qomariyah, {Nunung Nurul} and Kazakov, {Dimitar Lubomirov}",

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).; 27th International Conference on Inductive Logic Programming, ILP '17 ; Conference date: 04-09-2017 Through 06-09-2017",

year = "2018",

month = mar,

day = "29",

language = "English",

volume = "2085",

pages = "38--50",

editor = "Nicolas Lachiche and Christel Vrain",

booktitle = "Late Breaking Papers of the 27th International Conference on Inductive Logic Programming",

publisher = "CEUR Workshop Proceedings",

url = "https://ilp2017.sciencesconf.org",

}

Qomariyah, NN & Kazakov, DL 2018, Learning from Ordinal Data with Inductive Logic Programming in Description Logic. in N Lachiche & C Vrain (eds), Late Breaking Papers of the 27th International Conference on Inductive Logic Programming. vol. 2085, CEUR Workshop Proceedings, http://ceur-ws.org/Vol-2085/, pp. 38-50, 27th International Conference on Inductive Logic Programming, Orléans, France, 4/09/17. <http://ceur-ws.org/Vol-2085/>

Learning from Ordinal Data with Inductive Logic Programming in Description Logic. / Qomariyah, Nunung Nurul; Kazakov, Dimitar Lubomirov.

Late Breaking Papers of the 27th International Conference on Inductive Logic Programming. ed. / Nicolas Lachiche; Christel Vrain. Vol. 2085 http://ceur-ws.org/Vol-2085/ : CEUR Workshop Proceedings, 2018. p. 38-50.

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

TY - GEN

T1 - Learning from Ordinal Data with Inductive Logic Programming in Description Logic

AU - Qomariyah, Nunung Nurul

AU - Kazakov, Dimitar Lubomirov

N1 - Conference code: 27

PY - 2018/3/29

Y1 - 2018/3/29

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.

M3 - Conference contribution

VL - 2085

SP - 38

EP - 50

BT - Late Breaking Papers of the 27th International Conference on Inductive Logic Programming

A2 - Lachiche, Nicolas

A2 - Vrain, Christel

PB - CEUR Workshop Proceedings

CY - http://ceur-ws.org/Vol-2085/

T2 - 27th International Conference on Inductive Logic Programming

Y2 - 4 September 2017 through 6 September 2017

ER -