In today's network environment, the semantic gap between machine language and human language is the most important challenge of information management. Processing of text plays an important role in information management and knowledge management. In this paper, a proposed method shows how a text is related to its background knowledge. By background knowledge, People mean the parts of domain ontology which are not expressed in the text, but are shared by the creator and potential readers. Given the text-ontology mapping, people may discover the semantic domain of a text and how the text covers the domain knowledge. The semantic relatedness between the concepts mentioned in a text, as a whole unit,and the other concepts of the domain should be measured. This measure is based on the semantic relations defined by the ontology among its concepts. The experimental results prove that proposed method presents better overall performance and is natural way to improve retrieval results of users needed.
In order to reduce the time of fuzzy inference, the relevant matrices and the relationship matrices are used to constitute the fuzzy-valued concept networks. The elements of a relevant matrix represent the relevant degrees between concepts. The elements of a relationship matrix represent the relevant relationships between concepts. Fuzzy positive association relationship or fuzzy negative association relationship are used for formulating users queries in order to increase the flexibility of fuzzy information retrieval systems. Expanding the fuzzy-valued concept network architecture to the Internet environment, we propose a fuzzy information retrieval method based on the network-type fuzzy-valued concept network and it can be relatively more effective information retrieval in the distributed network
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