Abstract
In this paper, we propose the application of Machine Learning
(ML) methods to the Semantic Web (SW) as a mechanism to pre-
dict the correctness of semantic relations. For this purpose, we
have acquired a learning dataset from the SW and we have per-
formed an extensive experimental evaluation covering more than
1,800 relations of various types. We have obtained encouraging
results, reaching a maximum of 74.2% of correctly classified se-
mantic relations for classifiers able to validate the correctness of
multiple types of semantic relations (generic classifiers) and up to
98% for classifiers focused on evaluating the correctness of one
particular semantic relation (specialized classifiers)
(ML) methods to the Semantic Web (SW) as a mechanism to pre-
dict the correctness of semantic relations. For this purpose, we
have acquired a learning dataset from the SW and we have per-
formed an extensive experimental evaluation covering more than
1,800 relations of various types. We have obtained encouraging
results, reaching a maximum of 74.2% of correctly classified se-
mantic relations for classifiers able to validate the correctness of
multiple types of semantic relations (generic classifiers) and up to
98% for classifiers focused on evaluating the correctness of one
particular semantic relation (specialized classifiers)
Originalsprache | Englisch |
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Publikationsstatus | Veröffentlicht - 2010 |
Österreichische Systematik der Wissenschaftszweige (ÖFOS)
- 102
- 102001 Artificial Intelligence
- 102015 Informationssysteme
- 102022 Softwareentwicklung