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The ultimate aim of this paper is for semantic information retrieval. Especially, we concentrate on measure of concept similarity. There are 9 kinds of relation (arc) in WordNet to define concepts and we have a motive from that a value of each arc is different up to kind of relation. So, this paper first makes a fundamental for the value of relation between concepts. And we measure the arc-value based...
An aim of this research is to grasp related words of unknown word. Currently, several lexical dictionaries have been developed for semantic retrieval such as WordNet and FrameNet. However, more new words are created in every day because of new trends, new paradigm, new technology, etc. And, it is impossible to contain all of these new words. The existing methods, which grasp the meaning of unknown...
In this paper, we propose a topic selection method about Web documents. The idea of our approach is to utilize an ontology structure and TF (term frequency) values of each term. For improving the performance of documents clustering, our research is strongly demanded. We process Web documents for keywords acquisition using TF values and relevancy values between terms using relations defined in WordNet...
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