We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets and using this semantic information to restructure a taxonomy automatically generated from the Wikipedia system of categories. We evaluate against a manual gold standard and show that both category disambiguation and taxonomy restructuring perform with high accuracy. Besides, we assess these methods on automatically generated datasets and show that we are able to effectively enrich WordNet with a large number of instances from Wikipedia. Our approach produces an integrated resource, thus bringing together the fine-grained classification of instances in Wikipedia and a well-structured top-level taxonomy from WordNet.
Large-Scale Taxonomy Mapping for Restructuring and Integrating Wikipedia / Ponzetto, SIMONE PAOLO; Navigli, Roberto. - STAMPA. - (2009), pp. 2083-2088. (Intervento presentato al convegno Proc. of the 21th International Joint Conference on Artificial Intelligence (IJCAI 2009) tenutosi a Pasadena, California, USA nel July 14-17th 2009).
Large-Scale Taxonomy Mapping for Restructuring and Integrating Wikipedia
PONZETTO, SIMONE PAOLO;NAVIGLI, ROBERTO
2009
Abstract
We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets and using this semantic information to restructure a taxonomy automatically generated from the Wikipedia system of categories. We evaluate against a manual gold standard and show that both category disambiguation and taxonomy restructuring perform with high accuracy. Besides, we assess these methods on automatically generated datasets and show that we are able to effectively enrich WordNet with a large number of instances from Wikipedia. Our approach produces an integrated resource, thus bringing together the fine-grained classification of instances in Wikipedia and a well-structured top-level taxonomy from WordNet.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
