Enhancing Semantic Web Services with Inheritance

  • Simon Ferndriger
  • Abraham Bernstein
  • Jin Song Dong
  • Yuzhang Feng
  • Yuan-Fang Li
  • Jane Hunter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5318)


Currently proposed Semantic Web Services technologies allow the creation of ontology-based semantic annotations of Web services so that software agents are able to discover, invoke, compose and monitor these services with a high degree of automation. The OWL Services (OWL-S) ontology is an upper ontology in OWL language, providing essential vocabularies to semantically describe Web services. Currently OWL-S services can only be developed independently; if one service is unavailable then finding a suitable alternative would require an expensive and difficult global search/match. It is desirable to have a new OWL-S construct that can systematically support substitution tracing as well as incremental development and reuse of services. Introducing inheritance relationship (IR) into OWL-S is a natural solution. However, OWL-S, as well as most of the other currently discussed formalisms for Semantic Web Services such as WSMO or SAWSDL, has yet to define a concrete and self-contained mechanism of establishing inheritance relationships among services, which we believe is very important for the automated annotation and discovery of Web services as well as human organization of services into a taxonomy-like structure. In this paper, we extend OWL-S with the ability to define and maintain inheritance relationships between services. Through the definition of an additional “inheritance profile”, inheritance relationships can be stated and reasoned about. Two types of IRs are allowed to grant service developers the choice to respect the “contract” between services or not. The proposed inheritance framework has also been implemented and the prototype will be briefly evaluated as well.


Service Model Service Discovery Multiple Inheritance Service Annotation Inheritance Relationship 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Simon Ferndriger
    • 1
  • Abraham Bernstein
    • 1
  • Jin Song Dong
    • 2
  • Yuzhang Feng
    • 2
  • Yuan-Fang Li
    • 3
  • Jane Hunter
    • 3
  1. 1.Department of InformaticsUniversity of ZurichZurichSwitzerland
  2. 2.School of ComputingNational University of SingaporeSingapore
  3. 3.School of ITEEUniversity of QueenslandBrisbaneAustralia

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