A semantic role labelling-based framework for learning ontologies from Spanish documents

• Published in: Expert systems with applications Vol. 40; no. 6; pp. 2058 - 2068
• Main Authors: Ochoa, José Luis, Valencia-García, Rafael, Perez-Soltero, Alonso, Barceló-Valenzuela, Mario
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.05.2013; Elsevier
• Subjects: Applied sciences; Artificial intelligence; Axioms; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Digital; Education; Exact sciences and technology; Expert systems; Human; Information extraction; Information systems. Data bases; Knowledge base; Language theory and syntactical analysis; Learning; Memory organisation. Data processing; Ontology; Ontology learning; Semantic role labeling; Semantic Web; Semantics; Software; Speech and sound recognition and synthesis. Linguistics; Theoretical computing; Semantic Web; Information extraction; Semantic role labeling; Ontology; Ontology learning; Concept learning; Consumption; Semantic relation; Bottleneck; Semantic analysis; Termination problem; Knowledge representation; Web service; Knowledge base; Legibility; Semiautomatic; Semantic web; Semantics; World wide web; Internet; Rewriting systems
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.10.017

► Most of the information available in the Web is adapted primarily for human consumption. ► The Semantic Web deals with adding machine-readable information to Web pages. ► This paper proposes a new ontology learning methodology based on semantic role labeling. ► The method represents multiple semantic relations, taxonomic and partonomic in OWL 2.0. ► A set of experiments has been performed that show promising results. Currently, most of the information available in the Web is adapted primarily for human consumption, but there is so much information that can no longer be processed by a person in a reasonable time, either in digital or physical formats. To solve this problem, the idea of the Semantic Web arose. The Semantic Web deals with adding machine-readable information to Web pages. Ontologies represent a very important element of this web, as they provide a valid and robust structure to represent knowledge based on concepts, relations, axioms, etc. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semiautomatic methods to learn ontologies. In this sense, this paper proposes a new ontology learning methodology based on semantic role labeling from digital Spanish documents. The method makes it possible to represent multiple semantic relations specially taxonomic and partonomic ones in the standardized OWL 2.0. A set of experiments has been performed with the approach implemented in educational domain that show promising results.