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

doi:10.4018/IJSWIS.2018010101

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 journal › Article › peer-review

9 Citations (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 language	English
Article number	1
Number of pages	30
Journal	International Journal on Semantic Web and Information Systems
Volume	14
Issue number	1
DOIs	https://doi.org/10.4018/IJSWIS.2018010101
Publication status	Published - 1 Jan 2018

Keywords

ABox Reasoning
Machine Learning
Ontology
Performance Prediction
Semantic Web

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Cite this

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AU - Li, Yuan Fang

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Predicting reasoner performance on ABox intensive OWL 2 EL ontologies

Abstract

Keywords

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