Building ontologies is a difficult task requiring skills in logics and ontological analysis. Domain experts usually reach as far as organizing a set of concepts into a hierarchy in which the semantics of the relations is under-specified. The categorization of Wikipedia is a huge concept hierarchy of this form, covering a broad range of areas. We propose an automatic method for bootstrapping domain ontologies from the categories of Wikipedia. The method first selects a subset of concepts that are relevant for a given domain. The relevant concepts are subsequently split into classes and individuals, and, finally, the relations between the concepts are classified into subclass of, instance of, part of, and generic related to. We evaluate our method by generating ontology skeletons for the domains of Computing and Music. The quality of the generated ontologies has been measured against manually built ground truth datasets of several hundred nodes.
Bootstrapping Domain Ontologies from Wikipedia: A Uniform Approach / Mirylenka, Daniil; Passerini, Andrea; Serafini, Luciano. - In: IJCAI. - ISSN 1045-0823. - 2015-:(2015), pp. 1464-1470. ( 24th International Joint Conference on Artificial Intelligence, IJCAI 2015 Buenos Aires 25–31 July 2015).
Bootstrapping Domain Ontologies from Wikipedia: A Uniform Approach
Mirylenka, Daniil;Passerini, Andrea;Serafini, Luciano
2015-01-01
Abstract
Building ontologies is a difficult task requiring skills in logics and ontological analysis. Domain experts usually reach as far as organizing a set of concepts into a hierarchy in which the semantics of the relations is under-specified. The categorization of Wikipedia is a huge concept hierarchy of this form, covering a broad range of areas. We propose an automatic method for bootstrapping domain ontologies from the categories of Wikipedia. The method first selects a subset of concepts that are relevant for a given domain. The relevant concepts are subsequently split into classes and individuals, and, finally, the relations between the concepts are classified into subclass of, instance of, part of, and generic related to. We evaluate our method by generating ontology skeletons for the domains of Computing and Music. The quality of the generated ontologies has been measured against manually built ground truth datasets of several hundred nodes.| File | Dimensione | Formato | |
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