Towards context-aware semantic web service discovery through conceptual situation spaces

  • Authors:

  • Stefan Dietze;Alessio Gugliotta;John Domingue

  • Affiliations:

  • The Open University, Milton Keynes;The Open University, Milton Keynes;The Open University, Milton Keynes

  • Venue:
  • Proceedings of the 2008 international workshop on Context enabled source and service selection, integration and adaptation: organized with the 17th International World Wide Web Conference (WWW 2008)
  • Year:
  • 2008

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Abstract

Context-awareness is highly desired across several application domains. Semantic Web Services (SWS) enable the automatic discovery of distributed Web services based on comprehensive semantic representations. However, although SWS technology supports the automatic allocation of resources for a given well-defined task, it does not entail the discovery of appropriate SWS representations for a given situational context. Whereas tasks are highly dependent on the situational context in which they occur, SWS technology does not explicitly encourage the representation of domain situations. Moreover, describing the complex notion of a specific situation in all its facets is a costly task and may never reach semantic completeness. Particularly, following the symbolic SWS approach leads to ambiguity issues and does not entail semantic meaningfulness. Apart from that, not any real-world situation completely equals another, but has to be matched to a finite set of semantically defined parameter descriptions to enable context-adaptability. To overcome these issues, we propose Conceptual Situation Spaces (CSS) which are aligned to established SWS standards. CSS enable the description of situation characteristics as members in geometrical vector spaces following the idea of Conceptual Spaces. Semantic similarity between situations is calculated in terms of their Euclidean distance within a CSS. Extending merely symbolic SWS descriptions with context information on a conceptual level through CSS enables similarity-based matchmaking between real-world situation characteristics and predefined resource representations as part of SWS descriptions. To prove the feasibility, we apply our approach to the domain of E-Learning and provide a proof-of-concept prototype.