Predicting the quality of semantic relations by applying Machine Learning classiers

Miriam Fernandez, Reka Marta Sabou, Petr Knoth, Enrico Motta

Publikation: KonferenzbeitragKonferenzposter


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)
PublikationsstatusVeröffentlicht - 2010

Österreichische Systematik der Wissenschaftszweige (ÖFOS)

  • 102
  • 102001 Artificial Intelligence
  • 102015 Informationssysteme
  • 102022 Softwareentwicklung