Scalable semantic data processing has become a crucial issue for practical applications of the Semantic Web. In this paper, we propose an approach of scalable semantic data processing by knowledge summarization. The main idea is to express scalable semantic data on different abstraction and summarization levels to reduce their cardinalities, so that they can be processed efficiently. The notion of knowledge summarization is inspired from various techniques in granular computing and text summarization in computational linguistics. In this paper, we will present a formal framework of knowledge summarization for the Semantic Web and discuss how it can be used to improve the scalability of semantic data processing
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