Abstract
To realize web search engines with considering meaning of query phrases for each user, we have studied a method to extract hierarchical and synonymous relationships among tagged phrases on a social bookmark (SBM) for an individual SBM user. It detects the relationships from webpage clusters with same tagged phrases derived from the bookmarks shared in the target and his similar SBM users. However, noisy tagging violating personal phrase meaning degrades its detection accuracy. This chapter proposes a method to improve such drawback. The proposed method classifies webpages based on its content concordance as long as based on sameness of tagged phrases. Analyzing webpages belongingness to content-based and tag-based clusters, the relationships are detected more accurately. The experimental result shows the effectiveness of the proposed method.
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M. Ito, F. Harada, H. Shimakawa, Extracting ontology from tagging to web pages in similar user group. Int. J. Adv. Comput. Sci. 1(2), 58–64 (2011)
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Acknowledgments
Masaya Ito, who was a student of Graduate School of Science and Engineering, Ritsumeikan University until 2012, made enormous contribution to this research.
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Harada, F., Shimakawa, H. (2014). Personal Ontology Extraction Considering Content Concordance from Tagging to Webpages in Similar SBM Users. In: Lee, R. (eds) Applied Computing and Information Technology. Studies in Computational Intelligence, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-319-05717-0_10
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DOI: https://doi.org/10.1007/978-3-319-05717-0_10
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