Abstract
In this paper, we present a new model that incorporates the extensive knowledge derived from Wikipedia for cross-document knowledge discovery. The model proposed here is based on our previously introduced Concept Chain Queries (CCQ) which is a special case of text mining focusing on detecting semantic relationships between two concepts across multiple documents. We attempt to overcome the limitations of CCQ by building a semantic kernel for concept closeness computing to complement existing knowledge in text corpus. The experimental evaluation demonstrates that the kernel-based approach outperforms in ranking important chains retrieved in the search results.
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Yan, P., Jin, W. (2013). A New Approach for Improving Cross-Document Knowledge Discovery Using Wikipedia. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2013. Lecture Notes in Computer Science, vol 7934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38824-8_27
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DOI: https://doi.org/10.1007/978-3-642-38824-8_27
Publisher Name: Springer, Berlin, Heidelberg
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