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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
This paper proposes a system for finding a userpsilas interests on the Internet. It is based on his browsing behaviors and the contents of his visited pages. The system has two features. One is building userpsilas browsing interests implicitly, multiple keyword vectors, one per interest. The other is that it can
their historical and social context by understanding how the major topics associated with them have changed over time. Users can relate articles through time by examining the topical keywords that summarize a specific news event. By tracking the attention to a news article in the form of references in social media (such as
adaptive algorithm uses concept space and keywords in English. However, the content is available in Czech, Turkish and English languages. Multilingual content is presented in language-corresponding user interface. The first statistical results seem to be good, navigational pattern analysis found usual patterns of the
degree of relevancy for the user than is currently available with conventional methods, for example, using matching keywords. We describe here our method and the relation between the scenes and discuss a prototype system.
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