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Dynamic Topic Mapping Using Latent Semantic Indexing

Paper, was published by Frédéric Andrès and Motomu Naito at 2005-07-04

This paper proposes a dynamic multipoint of view of textual documents to produce topic maps.

External Link: IEEE record

This paper proposes an approach to provide a dynamic multipoint of view of textual documents based on summarization in arbitrary scale in order to produce topic maps. Our approach is based on the Latent Semantic Indexing (LSI) to deal with synonymy and polysemy. Textual resources are decomposed into a set of sentences and then summarized by a set of sentences that are similar to the view of user. A document may have various summaries and by consequence several topic maps according different user interests. The advantage of our method is to be independent to the language used in the source text. Our experimentation shows that the summary text can contains the sentences whose words are different from those used in the user view but their meanings are close to those used in the user point of view.

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As a former information scientist, I am fascinated since 1999 by the capabilities for building Topic Maps-based knowledge systems having the potential to augment human mind. One can model arbitrary knowledge organization systems, deal with semantic heterogeneity, collocate all facts about one subject in one logical place, and with TMQL have semantic retrieval on federated semantic networks. Therefore I expect bright prospects for business concepts building on the exchange of such knowledge snippets via semantic knowledge services.

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Topic Maps Lab auf der Cebit 2011
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