Issue No. 11 - November (2011 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.195
Elias Zavitsanos , NCSR "Demokritos", Patriarhou Gregoriou and Neapoleos St., Athens, Athens
Georgios Paliouras , NCSR "Demokritos", Patriarhou Gregoriou and Neapoleos St., Athens, Athens
George A. Vouros , University of the Aegean, Samos Island
This paper presents a method along with a set of measures for evaluating learned ontologies against gold ontologies. The proposed method transforms the ontology concepts and their properties into a vector space representation to avoid the common string matching of concepts and properties at the lexical layer. The proposed evaluation measures exploit the vector space representation and calculate the similarity of the two ontologies (learned and gold) at the lexical and relational levels. Extensive evaluation experiments are provided, which show that these measures capture accurately the deviations from the gold ontology. The proposed method is tested using the Genia and the Lonely Planet gold ontologies, as well as the ontologies in the benchmark series of the Ontology Alignment Evaluation Initiative.
Knowledge valuation, machine learning, concept learning, ontology design.
E. Zavitsanos, G. A. Vouros and G. Paliouras, "Gold Standard Evaluation of Ontology Learning Methods through Ontology Transformation and Alignment," in IEEE Transactions on Knowledge & Data Engineering, vol. 23, no. , pp. 1635-1648, 2010.