Predicting the quality of semantic relations by applying Machine Learning classifiers

Fernandez, Miriam; Sabou, Marta; Knoth, Petr and Motta, Enrico (2010). Predicting the quality of semantic relations by applying Machine Learning classifiers. In: EKAW 2010 - Knowledge Engineering and Knowledge Management by the Masses, 11-15 Oct 2010, Lisbon, Portugal.

Abstract

In this paper, we propose the application of Machine Learning (ML) methods to the Semantic Web (SW) as a mechanism to predict the correctness of semantic relations. For this purpose, we have acquired a learning dataset from the SW and we have performed 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 semantic 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).

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About

• Item ORO ID
• 26054
• Item Type
• Conference or Workshop Item
• Keywords
• semantic web; semantic relations; machine learning
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Research Group
• Centre for Research in Computing (CRC)
• Copyright Holders
• © Not known
• Depositing User
• Kay Dave