Ontology learning for cost-effective large-scale semantic annotation of web service interfaces

Authors:
Shahab Mokarizadeh;Peep Küngas;Mihhail Matskin
Affiliations:
Royal Institute of Technology, Stockholm, Sweden;University of Tartu, Tartu, Estonia;Royal Institute of Technology, Stockholm, Sweden and Norwegian University of Science and Technology, Trondheim, Norway
Venue:
EKAW'10 Proceedings of the 17th international conference on Knowledge engineering and management by the masses
Year:
2010

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Abstract

In this paper we introduce a novel unsupervised ontology learning approach, which can be used to automatically derive a reference ontology from a corpus of web services for annotating semantically the Web services in the absence of a core ontology. Our approach relies on shallow parsing technique from natural language processing in order to identify grammatical patterns of web service message element/part names and exploit them in construction of the ontology. The generated ontology is further enriched by introducing relationships between similar concepts. The experimental results on a set of global Web services indicate that the proposed ontology learning approach generates an ontology, which can be used to automatically annotate around 52% of element part and field names in a large corpus of heterogeneous Web services.