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As a practical and efficient technique to access XML data, keyword query approaches should address two main problems: enable users to express their query intentions accurately and provide matching algorithms to realize users' query intentions faithfully. Most of the existing researches concentrate on the second
Keyword search in RDBs has been extensively studied in recent years. The existing studies focused on finding all or top-k interconnected tuple-structures that contain keywords. In reality, the number of such interconnected tuple-structures for a keyword query can be large. It becomes very difficult for users to obtain
number of users with diverse characteristics and needs. Currently, many research projects or practical applications have emerged which only support single keyword search, and few of them support semantic retrieval. In this paper, we propose a model of ontology-based semantic information retrieval systems according to hybrid
process in which groups of semantically similar queries are identified. An efficient clustering algorithm called suffix tree clustering is developed in the study. Meanwhile, the keyword- based similarity measure is used for determining the closest cluster to the given query, and the Chinese synonymy is also considered in the
Keyword based information retrieval has difficulties in retrieving relevant information because it is not able to include the semantics of queries. In this paper, we present a novel method for query expansion based on semantic relations. In our proposed algorithm, semantically related words to the query are extracted
Current many information retrieval methods are based on purely keywords for representing the user needs. One of the main problems with this method is that it does not formally capture the explicit meaning of a keywords query, and ignores the documents that may be different in content but related with them. To improve
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