How can reasoner performance of ABox intensive ontologies be predicted?

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

doi:10.1007/978-3-319-50112-3_1

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 proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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 language	English
Title of host publication	Semantic Technology
Subtitle of host publication	6th Joint International Conference, JIST 2016, Revised Selected Papers
Editors	Y.-F. Li, W Hu, J S Dong, G Antoniou, Z Wang, J Sun, Y Liu
Publisher	Springer-Verlag
Pages	3-14
Number of pages	12
Volume	10055 LNCS
ISBN (Electronic)	9783319501123
ISBN (Print)	9783319501116
DOIs	https://doi.org/10.1007/978-3-319-50112-3_1
Publication status	Published - 2016
Event	6th Joint International Conference on Semantic Technology, JIST 2016 - Singapore, Singapore Duration: 2 Nov 2016 → 4 Nov 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10055 LNCS
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	6th Joint International Conference on Semantic Technology, JIST 2016
Country/Territory	Singapore
City	Singapore
Period	2/11/16 → 4/11/16

Keywords

Knowledge graph
Ontology reasoning
Practical reasoning
Prediction
Random forests
Semantic web

Access to Document

10.1007/978-3-319-50112-3_1Licence: Unspecified

Cite this

Guclu, I., Bobed, C., Pan, J. Z., Kollingbaum, M. J., & Li, Y. F. (2016). How can reasoner performance of ABox intensive ontologies be predicted? In Y-F. Li, W. Hu, J. S. Dong, G. Antoniou, Z. Wang, J. Sun, & Y. Liu (Eds.), Semantic Technology: 6th Joint International Conference, JIST 2016, Revised Selected Papers (Vol. 10055 LNCS, pp. 3-14). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10055 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-50112-3_1

How can reasoner performance of ABox intensive ontologies be predicted? / Guclu, Isa; Bobed, Carlos; Pan, Jeff Z.; Kollingbaum, Martin J.; Li, Yuan Fang.

Semantic Technology: 6th Joint International Conference, JIST 2016, Revised Selected Papers. ed. / Y.-F. Li; W Hu; J S Dong; G Antoniou; Z Wang; J Sun; Y Liu. Vol. 10055 LNCS Springer-Verlag, 2016. p. 3-14 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10055 LNCS).

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

Guclu, I, Bobed, C, Pan, JZ, Kollingbaum, MJ & Li, YF 2016, How can reasoner performance of ABox intensive ontologies be predicted? in Y-F Li, W Hu, JS Dong, G Antoniou, Z Wang, J Sun & Y Liu (eds), Semantic Technology: 6th Joint International Conference, JIST 2016, Revised Selected Papers. vol. 10055 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10055 LNCS, Springer-Verlag, pp. 3-14, 6th Joint International Conference on Semantic Technology, JIST 2016, Singapore, Singapore, 2/11/16. https://doi.org/10.1007/978-3-319-50112-3_1

Guclu I, Bobed C, Pan JZ, Kollingbaum MJ, Li YF. How can reasoner performance of ABox intensive ontologies be predicted? In Li Y-F, Hu W, Dong JS, Antoniou G, Wang Z, Sun J, Liu Y, editors, Semantic Technology: 6th Joint International Conference, JIST 2016, Revised Selected Papers. Vol. 10055 LNCS. Springer-Verlag. 2016. p. 3-14. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-50112-3_1

Guclu, Isa ; Bobed, Carlos ; Pan, Jeff Z. ; Kollingbaum, Martin J. ; Li, Yuan Fang. / How can reasoner performance of ABox intensive ontologies be predicted?. Semantic Technology: 6th Joint International Conference, JIST 2016, Revised Selected Papers. editor / Y.-F. Li ; W Hu ; J S Dong ; G Antoniou ; Z Wang ; J Sun ; Y Liu. Vol. 10055 LNCS Springer-Verlag, 2016. pp. 3-14 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "How can reasoner performance of ABox intensive ontologies be predicted?",

abstract = "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.",

keywords = "Knowledge graph, Ontology reasoning, Practical reasoning, Prediction, Random forests, Semantic web",

author = "Isa Guclu and Carlos Bobed and Pan, {Jeff Z.} and Kollingbaum, {Martin J.} and Li, {Yuan Fang}",

note = "Acknowledgments 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.; 6th Joint International Conference on Semantic Technology, JIST 2016 ; Conference date: 02-11-2016 Through 04-11-2016",

year = "2016",

doi = "10.1007/978-3-319-50112-3_1",

language = "English",

isbn = "9783319501116",

volume = "10055 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer-Verlag",

pages = "3--14",

editor = "Y.-F. Li and W Hu and Dong, {J S} and G Antoniou and Z Wang and J Sun and Y Liu",