How can reasoner performance of ABox intensive ontologies be predicted?

Isa Guclu, Carlos Bobed, Jeff Z. Pan*, Martin J. Kollingbaum, Yuan Fang Li

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

1 Citation (Scopus)


Reasoner performance prediction of ontologies in OWL 2 language has been studied so far from different dimensions. One key aspect of these studies has been the prediction of how much time a particular task for a given ontology will consume. Several approaches have adopted different machine learning techniques to predict time consumption of ontologies already. However, these studies focused on capturing general aspects of the ontologies (i.e., mainly the complexity of their TBoxes), while paying little attention to ABox intensive ontologies. To address this issue, in this paper, we propose to improve the representativeness of ontology metrics by developing new metrics which focus on the ABox features of ontologies. Our experiments show that the proposed metrics contribute to overall prediction accuracy for all ontologies in general without causing side-effects.

Original languageEnglish
Title of host publicationSemantic Technology
Subtitle of host publication6th Joint International Conference, JIST 2016, Revised Selected Papers
EditorsY.-F. Li, W Hu, J S Dong, G Antoniou, Z Wang, J Sun, Y Liu
Number of pages12
Volume10055 LNCS
ISBN (Electronic)9783319501123
ISBN (Print)9783319501116
Publication statusPublished - 2016
Event6th Joint International Conference on Semantic Technology, JIST 2016 - Singapore, Singapore
Duration: 2 Nov 20164 Nov 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10055 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference6th Joint International Conference on Semantic Technology, JIST 2016

Bibliographical note

This work was partially supported by the EC Marie Curie K-Drive project (286348), the CICYT project (TIN2013-46238-C4-4-R) and the DGA-FSE project.


  • Knowledge graph
  • Ontology reasoning
  • Practical reasoning
  • Prediction
  • Random forests
  • Semantic web


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