ABSTRACT
Matching entities from different information sources is a very important problem in data analysis and data integration. It is, however, challenging due to the number and diversity of information sources involved, and the significant editorial efforts required to collect sufficient training data. In this paper, we present an approach that leverages user clicks during Web search to automatically generate training data for entity matching. The key insight of our approach is that Web pages clicked for a given query are likely to be about the same entity. We use random walk with restart to reduce data sparseness, rely on co-clustering to group queries and Web pages, and exploit page similarity to improve matching precision. Experimental results show that: (i) With 360K pages from 6 major travel websites, we obtain 84K matchings (of 179K pages) that refer to the same entities, with an average precision of 0.826; (ii) The quality of matching obtained from a classifier trained on the resulted seed data is promising: the performance matches that of editorial data at small size and improves with size.
- R. Baeza-Yates and A. Tiberi. Extracting semantic relations fromquery logs. In KDD, 2002. Google ScholarDigital Library
- M. Bilenko, B. Kamath, and R. J. Mooney. Adaptive blocking: Learning to scale up record linkage. In ICDM, 2006. Google ScholarDigital Library
- M. Bilenko and R. J. Mooney. Adaptive duplicate detection using learnable string similarity measures. In SIGKDD, 2003. Google ScholarDigital Library
- B. Billerbeck, G. Demartini, C. S. Firan, T. Iofciu, and R. Krestel. Ranking entities using web search query logs. In ECDL, 2010. Google ScholarDigital Library
- H. Cao, D. Jiang, J. Pei, Q. He, Z. Liao, E. Chen, and H. Li. Context-aware query suggestion by mining click-through and session data. In KDD, 2008. Google ScholarDigital Library
- D. Chakrabarti and R. R. Mehta. The paths more taken: matching DOM trees to search logs for accurate webpage clustering. In WWW, 2010. Google ScholarDigital Library
- T. F. Coleman and J. J. Moré. Estimation of sparse Jacobian matrices and graph coloring problems. SIAM Journal on Numerical Analysis, 1983.Google Scholar
- T. H. Cormen, C. Stein, R. L. Rivest, and C. E. Leiserson. Introduction to Algorithms. McGraw-Hill, 2001. Google ScholarDigital Library
- N. Craswell and M. Szummer. Random walks on the click graph. In SIGIR, 2007. Google ScholarDigital Library
- I. S. Dhillon. Co-clustering documents and words using bipartite spectral graph partitioning. In SIGKDD, 2001. Google ScholarDigital Library
- R. B. Doorenbos, O. Etzioni, and D. S. Weld. A scalable comparison-shopping agent for the world-wide web. In AGENTS, 1997. Google ScholarDigital Library
- C. F. Dorneles, R. Gonçalves, and R. dos Santos Mello. Approximate data instance matching: a survey. Knowledge and Information Systems, 2011. Google ScholarDigital Library
- L. Getoor and A. Machanavajjhala. Entity resolution: theory, practice and open challenges. PVLDB, 2012. Google ScholarDigital Library
- J. Greiner. A comparison of parallel algorithms for connected components. In SPAA, 1994. Google ScholarDigital Library
- C. Kang, S. Vadrevu, R. Zhang, R. v. Zwol, L. G. Pueyo, N. Torzec, J. He, and Y. Chang. Ranking related entities for web search queries. In WWW, 2011. Google ScholarDigital Library
- H. Köpcke and E. Rahm. Frameworks for entity matching: A comparison. Data Knowl. Eng., 69(2):197--210, 2010. Google ScholarDigital Library
- G. Malewicz, M. H. Austern, A. J. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski. Pregel: a system for large-scale graph processing. In SIGMOD, 2010. Google ScholarDigital Library
- P. N. Mendes, P. Mika, H. Zaragoza, and R. Blanco. Measuring website similarity using an entity-aware click graph. In CIKM, 2012. Google ScholarDigital Library
- J.-Y. Pan, H.-J. Yang, C. Faloutsos, and P. Duygulu. Automatic multimedia cross-modal correlation discovery. In SIGKDD, 2004. Google ScholarDigital Library
- V. Rastogi, N. Dalvi, and M. Garofalakis. Large-scale collective entity matching. PVLDB, 2011. Google ScholarDigital Library
- S. Tejada, C. A. Knoblock, and S. Minton. Learning object identification rules for information integration. Inf. Syst., 26(8):607--633, Dec. 2001. Google ScholarDigital Library
- H. Tong, C. Faloutsos, and J.-Y. Pan. Fast random walk with restart and its applications. In ICDM, 2006. Google ScholarDigital Library
- C. Wang, F. Jing, L. Zhang, and H.-J. Zhang. Image annotation refinement using random walk with restarts. In ACM MM, 2006. Google ScholarDigital Library
- J. Yi and F. Maghoul. Query clustering using click-through graph. In WWW, 2009. Google ScholarDigital Library
Index Terms
- Exploiting user clicks for automatic seed set generation for entity matching
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