| Autor: |
Guclu, Isa, Bobed Lisbona, Carlos, Pan, Jeff Z. Pan, Kollingbaum, Martin J., Li, Yuan-Fang |
| Jazyk: |
angličtina |
| Rok vydání: |
2016 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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