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Semantic similarity measurement using historical google search patterns

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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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  1. http://www.abs.gov.au/

  2. http://marimba.d.umn.edu/

References

  • Aitken, A. (2007). Statistical mathematics. Oliver & Boyd.

  • Badea, B., & Vlad, A. (2006). Revealing Statistical Independence of Two Experimental Data Sets: An Improvement on Spearman’s Algorithm. In ICCSA (pp. 1166–1176).

  • Banek, M., Vrdoljak, B., Min Tjoa, A., Skocir, Z. (2007). Automating the Schema Matching Process for Heterogeneous Data Warehouses. In DaWaK (pp. 45–54). 596

  • Banek, M., Vrdoljak, B., Tjoa, A.M. (2007). Using Ontologies for Measuring Semantic Similarity in Data Warehouse Schema Matching Process. In CONTEL (pp. 227–234).

  • Banerjee, S., & Pedersen, T. (2003). Extended Gloss Overlaps as a Measure of Semantic Relatedness. In IJCAI (pp. 805–810).

  • Bollegala, D., Matsuo, Y., Ishizuka, M. (2007). Measuring semantic similarity between words using web search engines. In WWW (pp. 757–766).

  • Bollegala, D., Honma, T., Matsuo, Y., Ishizuka, M. (2008). Mining for personal name aliases on the web. In WWW (pp. 1107–1108).

  • Brin, S., & Page, L. (1998). The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks, 30(1–7), 107–117.

    Google Scholar 

  • Budanitsky, A., & Hirst, G. (2006). Evaluating WordNet-based Measures of Lexical Semantic Relatedness. Computational Linguistics, 32(1), 13–47.

    Article  Google Scholar 

  • Choi, H., & Varian, H. (2009). Predicting the present with Google Trends. Technical Report, Economics Research Group, Google.

  • Cilibrasi, R., & Vitányi, P.M. (2007). The Google Similarity Distance. IEEE Transactions on Knowledge and Data Engineering, 19(3), 370–383.

    Article  Google Scholar 

  • Dhurandhar, A. (2011). Improving predictions using aggregate information. In KDD (pp. 1118–1126).

  • Egghe, L., & Leydesdorff, L. (2009). The relation between Pearson’s correlation coefficient r and Salton’s cosine measure CoRR abs/0911.1318.

  • Fong, J., Shiu, H., Cheung, D. (2009). A relational-XML data warehouse for data aggregation with SQL and XQuery. Software, Practice and Experience, 38(11), 1183–1213.

    Article  Google Scholar 

  • Grubbs, F. (1969). Procedures for Detecting Outlying Observations in Samples. Technometrics, 11(1), 1–21.

    Article  Google Scholar 

  • Hliaoutakis, A., Varelas, G., Petrakis, E.G.M.,Milios, E. (2006). Med-Search: A Retrieval System for Medical Information Based on Semantic Similarity. In ECDL (pp. 512–515).

  • Hu, N., Bose, I., Koh, N.S., Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems (DSS), 52(3), 674–684.

    Article  Google Scholar 

  • Hjorland, H. (2007). Semantics and knowledge organization. ARIST, 41(1), 367–405.

    Google Scholar 

  • Jung, J.J., & Thanh Nguyen, N. (2008). Collective Intelligence for Semantic and Knowledge Grid. Journal of Universal Computer Science (JUCS), 14(7), 1016–1019.

    Google Scholar 

  • Kopcke, H., Thor, A., Rahm, E. (2010). Evaluation of entity resolution approaches on real-world match problems. PVLDB, 3(1), 484–493.

    Google Scholar 

  • Leacock, C., Chodorow, M., Miller, G.A. (1998). Using Corpus Statistics and WordNet Relations for Sense Identification. Computational Linguistics, 24(1), 147–165.

    Google Scholar 

  • Lesk, M. (1986). Information in Data: Using the Oxford English Dictionary on a Computer. SIGIR Forum, 20(1–4), 18–21.

    Article  Google Scholar 

  • Li, J., Alan Wang, G., Chen, H. (2011). Identity matching using personal and social identity features. Information Systems Frontiers, 13(1), 101–113.

    Article  Google Scholar 

  • Li, Y., Bandar, A., McLean, D. (2003). An approach for Measuring Semantic Similarity between Words Using Multiple Information Sources. IEEE Transactions on Knowledge and Data Engineering, 15(4), 871–882.

    Article  Google Scholar 

  • Liu, B., & Zhang, L. (2012). A Survey of Opinion Mining and Sentiment Analysis. In Mining Text Data (pp. 415–463).

  • Miller, G., & Charles, W. (1991). Contextual Correlates of Semantic Similarity. Language and Cognitive Processes, 6(1), 1–28.

    Article  Google Scholar 

  • Nandi, A., & Bernstein, P.A. (2009). HAMSTER: Using Search Click- logs for Schema and Taxonomy Matching. PVLDB, 2(1), 181–192.

    Google Scholar 

  • Patuwo, B.E., & Hu, M. (1998) Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14(1), 35–62.

    Article  Google Scholar 

  • Patwardhan, S., Banerjee, S., Pedersen, T. (2003). Using Measures of Semantic Relatedness for Word Sense Disambiguation. In CICLing (pp. 241–257).

  • Pedersen, T., Patwardhan, S., Michelizzi, J. (2004). Word-Net::Similarity - Measuring the Relatedness of Concepts. In AAAI (pp. 1024–1025).

  • Petrakis, E.G.M., Varelas, G., Hliaoutakis, A., Raftopoulou, P. (2006). X-Similarity: Computing Semantic Similarity between Concepts from Different Ontologies. JDIM, 4(4), 233–237.

    Google Scholar 

  • Pirro, G. (2009). A semantic similarity metric combining features and intrinsic information content. Data and Knowledge Engineering, 68(11), 1289–1308.

    Article  Google Scholar 

  • Resnik, P. (1995). Using Information Content to Evaluate Semantic Similarity in a Taxonomy. In IJCAI (pp. 448–453).

  • Retzer, S., Yoong, P., Hooper, V. (2012). Inter-organisational knowledge transfer in social networks: A definition of intermediate ties. Information Systems Frontiers, 14(2), 343–361.

    Article  Google Scholar 

  • Rousseeuw, P.J., & Leroy, A.M. (2005). Robust Regression and Outlier Detection: John Wiley & Sons Inc.

  • Sanchez, D., Batet, M., Valls, A. (2010). Web-Based Semantic Similarity: An Evaluation in the Biomedical Domain. International Journal of Software and Informatics, 4(1), 39–52.

    Google Scholar 

  • Sanchez, D., Batet, M., Valls, A., Gibert, K. (2010). Ontology-driven web-based semantic similarity. Journal of Intelligent Information Systems, 35(3), 383–413.

    Article  Google Scholar 

  • Scarlat, E., & Maries, I. (2009). Towards an Increase of Collective Intelligence within Organizations Using Trust and Reputation Models. In ICCCI (pp. 140–151).

  • Sparck Jones, K. (2006). Collective Intelligence: It’s All in the Numbers. IEEE Intelligent Systems (EXPERT), 21(3), 64–65.

    Article  Google Scholar 

  • Tuan Duc, N., Bollegala, D., Ishizuka, M. (2010). Using Relational Similarity between Word Pairs for Latent Relational Search on the Web. In Web Intelligence (pp. 196–199).

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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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Correspondence to Jorge Martinez-Gil.

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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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