Predicting reasoner performance on ABox intensive OWL 2 EL ontologies

Jeff Z. Pan, Carlos Bobed, Isa Guclu, Fernando Bobillo, Martin Kollingbaum, Eduardo Mena, Yuan Fang Li

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this article, the authors introduce the notion of ABox intensity in the context of predicting reasoner performance to improve the representativeness of ontology metrics, and they develop new metrics that focus on ABox features of OWL 2 EL ontologies. Their experiments show that taking into account the intensity through the proposed metrics contributes to overall prediction accuracy for ABox intensive ontologies.

Original languageEnglish
Pages (from-to)1-30
Number of pages30
JournalInternational Journal on Semantic Web and Information Systems
Volume14
Issue number1
DOIs
Publication statusPublished - 1 Jan 2018

Fingerprint

Ontology
Experiments

Keywords

  • ABox Reasoning
  • Machine Learning
  • Ontology
  • Performance Prediction
  • Semantic Web

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

Cite this

Predicting reasoner performance on ABox intensive OWL 2 EL ontologies. / Pan, Jeff Z.; Bobed, Carlos; Guclu, Isa; Bobillo, Fernando; Kollingbaum, Martin; Mena, Eduardo; Li, Yuan Fang.

In: International Journal on Semantic Web and Information Systems, Vol. 14, No. 1, 01.01.2018, p. 1-30.

Research output: Contribution to journalArticle

Pan, Jeff Z. ; Bobed, Carlos ; Guclu, Isa ; Bobillo, Fernando ; Kollingbaum, Martin ; Mena, Eduardo ; Li, Yuan Fang. / Predicting reasoner performance on ABox intensive OWL 2 EL ontologies. In: International Journal on Semantic Web and Information Systems. 2018 ; Vol. 14, No. 1. pp. 1-30.
@article{2999beb9df744a93b3f364cc039e8572,
title = "Predicting reasoner performance on ABox intensive OWL 2 EL ontologies",
abstract = "In this article, the authors introduce the notion of ABox intensity in the context of predicting reasoner performance to improve the representativeness of ontology metrics, and they develop new metrics that focus on ABox features of OWL 2 EL ontologies. Their experiments show that taking into account the intensity through the proposed metrics contributes to overall prediction accuracy for ABox intensive ontologies.",
keywords = "ABox Reasoning, Machine Learning, Ontology, Performance Prediction, Semantic Web",
author = "Pan, {Jeff Z.} and Carlos Bobed and Isa Guclu and Fernando Bobillo and Martin Kollingbaum and Eduardo Mena and Li, {Yuan Fang}",
year = "2018",
month = "1",
day = "1",
doi = "10.4018/IJSWIS.2018010101",
language = "English",
volume = "14",
pages = "1--30",
journal = "International Journal on Semantic Web and Information Systems (IJSWIS)",
issn = "1552-6283",
publisher = "IGI Publishing",
number = "1",

}

TY - JOUR

T1 - Predicting reasoner performance on ABox intensive OWL 2 EL ontologies

AU - Pan, Jeff Z.

AU - Bobed, Carlos

AU - Guclu, Isa

AU - Bobillo, Fernando

AU - Kollingbaum, Martin

AU - Mena, Eduardo

AU - Li, Yuan Fang

PY - 2018/1/1

Y1 - 2018/1/1

N2 - In this article, the authors introduce the notion of ABox intensity in the context of predicting reasoner performance to improve the representativeness of ontology metrics, and they develop new metrics that focus on ABox features of OWL 2 EL ontologies. Their experiments show that taking into account the intensity through the proposed metrics contributes to overall prediction accuracy for ABox intensive ontologies.

AB - In this article, the authors introduce the notion of ABox intensity in the context of predicting reasoner performance to improve the representativeness of ontology metrics, and they develop new metrics that focus on ABox features of OWL 2 EL ontologies. Their experiments show that taking into account the intensity through the proposed metrics contributes to overall prediction accuracy for ABox intensive ontologies.

KW - ABox Reasoning

KW - Machine Learning

KW - Ontology

KW - Performance Prediction

KW - Semantic Web

UR - http://www.scopus.com/inward/record.url?scp=85038601576&partnerID=8YFLogxK

U2 - 10.4018/IJSWIS.2018010101

DO - 10.4018/IJSWIS.2018010101

M3 - Article

VL - 14

SP - 1

EP - 30

JO - International Journal on Semantic Web and Information Systems (IJSWIS)

JF - International Journal on Semantic Web and Information Systems (IJSWIS)

SN - 1552-6283

IS - 1

ER -