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Interleaving Reasoning and Selection with Semantic Data

The scalability is a crucial topic for practical applications of the Semantic Web, because of the extremely large scale data on the Web. In this paper we propose a general framework of interleaving reasoning and selection with semantic data. The main idea of the interleaving framework is to use some selectors to select only limited and relevant part of data for reasoning, so that the efficiency and the scalability of reasoning can be improved. In this paper, we examine how the interleaving framework can be achieved within the LarKC platform, a platform for massive distributed incomplete reasoning that will remove the scalability barriers of currently existing reasoning systems for the Semantic Web. We discuss the implementation of variant interleaving approaches within the LarKC platform.