Abstract
Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is an important challenge in the information integration field. The problem is that techniques for textual semantic similarity measurement often fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem by determining the semantic similarity for terms using the knowledge inherent in the search history logs from the Google search engine. To do this, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons. We have shown experimentally that some of these methods correlate well with respect to human judgment when evaluating general purpose benchmark datasets, and significantly outperform existing methods when evaluating datasets containing terms that do not usually appear in dictionaries.
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Acknowledgements
We would like to to thank the reviewers for their time and consideration. We thank Lisa Huckfield for proofreading this manuscript. This work has been funded by Spanish Ministry of Innovation and Science through: REALIDAD: Efficient Analysis, Management and Exploitation of Linked Data., Project Code: TIN2011-25840 and by the Department of Innovation, Enterprise and Science from the Regional Government of Andalucia through: Towards a platform for exploiting and analyzing biological linked data, Project Code: P11-TIC-7529.
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Martinez-Gil, J., Aldana-Montes, J.F. Semantic similarity measurement using historical google search patterns. Inf Syst Front 15, 399–410 (2013). https://doi.org/10.1007/s10796-012-9404-7
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DOI: https://doi.org/10.1007/s10796-012-9404-7