No abstract

No abstract

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The traditional "publish-find-bind" architecture for web services paid little attention to the problem of communication and knowledge sharing between the service providers and the service consumers. This problem is very important because, in order to discover required services effectively and efficiently, the consumers need to know more about the published services. We should consider how customers represent their requirements and how service providers represent their services, and hence to bridge the knowledge gap between the service providers and the service consumers. This paper investigates the use of structured description and representation of natural language in the matchmaking of web-services with customer requirements. A semantic framework for the representation of such descriptions is motivated based upon the limitations of current approaches. A matchmaking algorithm is given along with a concrete example of how the matchmaking process may be applied.

The knowledge grid (KG) has gradually been becoming a keen research topic in the joint field of the Semantic Web, the Semantic Grid, and the Web Services for its potential impact in making full use of various resources, being it conceptual or physical, from the web and grid. Many research activities in this direction focus on the architecture and its components, and base themselves technically on data mining technologies or the like, which obviously considers (web) information systems to be a foundational layer for the grid and web. However, further study on the web has revealed that the web cannot, and unnecessarily, be viewed simply as a closed world model (CWM). The web is an incomplete and inaccurate (knowledge) system, from which we will seek answers regardless their inconsistency. In this paper, we will first investigate the current researches into the knowledge grid, then discuss the problems of incompleteness of knowledge processing and reasoning and propose a category theory based model to handle such incomplete knowledge, and finally provide an analysis of the model.