Ontology Learning for Cost-Effective Large-Scale Semantic Annotation of Web Service Interfaces

  • Shahab Mokarizadeh
  • Peep Küngas
  • Mihhail Matskin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6317)

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.

Keywords

Ontology Learning Web Services Annotation NLP 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Shahab Mokarizadeh
    • 1
  • Peep Küngas
    • 2
  • Mihhail Matskin
    • 1
    • 3
  1. 1.Royal Institute of Technology (KTH)StockholmSweden
  2. 2.University of TartuTartuEstonia
  3. 3.Norwegian University of Science and Technology (NTNU)TrondheimNorway

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