Contexts for the Semantic Web

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Abstract

A central theme of the Semantic Web is that programs should be able to easily aggregate data from different sources. Unfortunately, even if two sites provide their data using the same data model and vocabulary, subtle differences in their use of terms and in the assumptions they make pose challenges for aggregation. Experiences with the TAP project reveal some of the phenomena that pose obstacles to a simplistic model of aggregation. Similar experiences have been reported by AI projects such as Cyc, which has led to the development and use of various context mechanisms. In this paper we report on some of the problems with aggregating independently published data and propose a context mechanism to handle some of these problems. We briefly survey the context mechanisms developed in AI and contrast them with the requirements of a context mechanism for the Semantic Web. Finally, we present a context mechanism for the Semantic Web that is adequate to handle the aggregation tasks, yet simple from both computational and model theoretic perspectives.