@inproceedings{5eabc09fbfd54438b7fbf419c93beb09,
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",
booktitle = "Semantic Technology",
}