A pragmatic approach for the semantic description and matching of pervasive resources

Abstract

Abstract. The increasing popularity of personal wireless devices has raised new demands for the efficient discovery of heterogeneous devices and services in pervasive environments. With the advancement of the electronic world, the diversity of available services is increasing rapidly. Traditional approaches for service discovery describe services at a syntactic level and the matching mechanisms available for these approaches are limited to syntactic comparisons based on attributes or interfaces. In order to overcome these limitations, there has been an increased interest in the use of semantic description and matching techniques to support effective service discovery. In this paper, we present a semantic matching approach to facilitate the discovery of device-based services in pervasive environments. The approach includes a ranking mechanism that orders services according to their suitability and also considers priorities placed on individual requirements in a request during the matching process. The solution has been systematically evaluated for its retrieval effectiveness and the results have shown that the matcher results agree reasonably well with human judgement. Another important practical concern is the efficiency and the scalability of the semantic matching solution. Therefore, we have evaluated the scalability of the proposed solution by investigating the variation in matching time in response to increasing numbers of advertisements and increasing request sizes, and have presented the empirical results.

Spatial Coordination of Pervasive Services through Chemical-Inspired Tuple Spaces

“…Currently, we are studying an architecture for tuple spaces which can allow to plug semantic match algorithms in an easy and flexible way. Indeed, different applications may require different semantic approaches, from expressive but costly ones like [Bobillo and Straccia 2008], to more light approaches such as [Bandara et al 2008].…”

“…It should be noted that matching details are orthogonal to our model, since the application at hand may require a specific implementation of them, ranging from expressive though costly approaches like [Bobillo and Straccia 2008], to more lightweight ones like [Bandara et al 2008]. We only assume that matching (which can even change over time, e.g.…”

“…This is because we want to express general reactions that apply to specific tuples independently of their syntactic structure, which cannot be clearly foreseen at design time. Accordingly, semantic matching can be considered as a proper matching criterion for our coordination infrastructure [Paolucci et al 2002;Bandara et al 2008;Bobillo and Straccia 2008;Tolksdorf et al 2008;Giunchiglia et al 2007].…”

Enhancing Grid Service Discovery with a Semantic Wiki and the Concept Matching Approach

“…Their similarity score can be obtained using the percentage deviation from the requested value or a fuzzy membership function, depending on the individual service description and the user requirement. An example of the fuzzy membership function is discussed in [15], in which authors illustrate an implementation when considering the numeric constrains as fuzzy boundaries and define functions for calculating similarity scores.…”

Spatial Coordination of Pervasive Systems through Chemical-Inspired Tuple Spaces

“…in ontology-based web matchmaking [11], [12]), instead of syntactically. Accordingly, chemical laws (and hence the overall system behaviour) support opennes and context-dependency for they are fired depending on the degree of semantic match with tuples, hence they can be seamlessly influenced by the ontology of the application domain, by contextual information in each location, service/request match, user profiles, and so on.…”