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 |
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Title of host publication | Semantic Technology |
Subtitle of host publication | 6th Joint International Conference, JIST 2016, Singapore, Singapore, November 2-4, 2016, Revised Selected Papers |
Editors | Yuan-Fang Li, Wei Hu, Jin Song Dong, Grigoris Antoniou, Zhe Wang, Jun Sun, Yang Liu |
Place of Publication | Cham, Switzerland |
Publisher | Springer |
Pages | 3-14 |
Number of pages | 12 |
ISBN (Electronic) | 9783319501123 |
ISBN (Print) | 9783319501116 |
DOIs | |
Publication status | Published - 2016 |
Event | Joint International Conference on Semantic Technology 2016 - Singapore, Singapore Duration: 2 Nov 2016 → 4 Nov 2016 Conference number: 6th https://link-springer-com.ezproxy.lib.monash.edu.au/book/10.1007%2F978-3-319-50112-3 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 10055 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Joint International Conference on Semantic Technology 2016 |
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Abbreviated title | JIST 2016 |
Country/Territory | Singapore |
City | Singapore |
Period | 2/11/16 → 4/11/16 |
Internet address |
Keywords
- Semantic web
- Ontology reasoning
- Prediction
- Random forests
- Knowledge graph
- Practical reasoning