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Traditional information gathering systems are mostly keyword-based that are lack of semantic comprehension and analysis ability and can't guarantee the comprehensiveness and accuracy of information gathering. This paper proposes Chinese patent information gathering model based on domain ontology, which can visualize
Although arguable success of today's keyword based search engines in certain information retrieval tasks, ranking search results in a meaningful way remains an open problem. In this work, the goal is to use of semantic relationships for ranking documents without relying on the existence of any specific structure in a
competitors, track the developed trend of the industrial technique and improve the independent innovation capability and core competitiveness of their own. But at present patent information retrieval systems based on the keyword match can't satisfy the users' demands on accuracy and completeness. So, this paper proposes to
This correspondence presents a novel hierarchical clustering approach for knowledge document self-organization, particularly for patent analysis. Current keyword-based methodologies for document content management tend to be inconsistent and ineffective when partial meanings of the technical content are used for
based on ontology. It uses the rich semantic knowledge of ontology to upgrade the retrieval based on keywords to concepts, and combines it with the specialized engine to improve retrieval effect and efficiency. The paper also takes patent information for example to explain its application at the end.
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