An approach to support Web service classification and annotation

The need for supporting the classification and semantic annotation of services constitutes an important challenge for service-centric software engineering. Late-binding and, in general, service matching approaches, require services to be semantically annotated. Such a semantic annotation may require, in turn, to be made in agreement to a specific ontology. Also, a service description needs to properly relate with other similar services. This paper proposes an approach to i) automatically classify services to specific domains and ii) identify key concepts inside service textual documentation, and builds a lattice of relationships between service annotations. Support vector machines and formal concept analysis have been used to perform the two tasks. Results obtained classifying a set of Web services show that the approach can provide useful insights in both service publication and service retrieval phases.

[1]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[2]  David D. Lewis,et al.  Representation and Learning in Information Retrieval , 1991 .

[3]  Mark A. Musen,et al.  The Knowledge Model of Protégé-2000: Combining Interoperability and Flexibility , 2000, EKAW.

[4]  Julio Gonzalo,et al.  Browsing Search Results via Formal Concept Analysis: Automatic Selection of Attributes , 2004, ICFCA.

[5]  Paul Compton,et al.  Formal Concept Analysis for Domain-Specific Document Retrieval Systems , 2001, Australian Joint Conference on Artificial Intelligence.

[6]  Thorsten Joachims,et al.  A Statistical Learning Model of Text Classification for Support Vector Machines. , 2001, SIGIR 2002.

[7]  Jin Yuan-ping Using Formal Concept Analysis for Ontology Building , 2005 .

[8]  Steffen Staab,et al.  Deriving Concept Hierarchies from Text by Smooth Formal Concept Analysis , 2003 .

[9]  L. R. Rasmussen,et al.  In information retrieval: data structures and algorithms , 1992 .

[10]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[11]  Yiming Yang,et al.  A re-examination of text categorization methods , 1999, SIGIR '99.

[12]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[13]  Hele-Mai Haav,et al.  An Application of Inductive Concept Analysis to Construction of Domain-specific Ontologies , 2003 .

[14]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[15]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[16]  Massimiliano Di Penta,et al.  An approach to classify software maintenance requests , 2002, International Conference on Software Maintenance, 2002. Proceedings..

[17]  Gerd Stumme,et al.  FCA-MERGE: Bottom-Up Merging of Ontologies , 2001, IJCAI.

[18]  Bernhard Ganter,et al.  Formal Concept Analysis , 1999, Springer Berlin Heidelberg.

[19]  Thorsten Joachims,et al.  A statistical learning learning model of text classification for support vector machines , 2001, SIGIR '01.

[20]  Takahiro Kawamura,et al.  Semantic Matching of Web Services Capabilities , 2002, SEMWEB.