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more accurately and simply. RDF is a language for representing metadata. An enormous number of keywords on the semantic Web are very important to make practical applications of the semantic Web because most users prefer to search with keywords. In this paper, we classify queries with keyword conditions into three patterns
In this paper, we introduce a novel method based on context awareness, semantic similarities and customized weights for different categories to improve keyword matching. The algorithm is able to weight terms by using category information and semantic relationships with WordNet as a lexical database. To demonstrate the
Most of the current focused crawling approaches perform syntactic matching, that is, they retrieve documents that contain particular keywords from the user's query. This often leads to poor discovery results, because the keywords in the query can be semantically similar but syntactically different, or vice-versa
subsequently used for evaluating the semantic document similarity and document quality. As such, the precision criterion is employed for efficient evaluations by comparing with keywords-based search method. The experimental results reported that the proposed method was able to outperform the TF/IDF method up to 9% on average
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