Entity disambiguation in anonymized graphs using graph kernels

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2013, Pages 1037-1046

  Hermansson, Linus ^a   Kerola, Tommi ^a   Johansson, Fredrik ^a   Jethava, Vinay ^a   Dubhashi, Devdatt ^a  

^a CHALMERS UNIVERSITY OF TECHNOLOGY   (Sweden)

Author keywords

Entity disambiguation; Entity resolution; Graph kernels; Support vector machines

Indexed keywords

CLASSIFICATION TASKS; DESIGN AND IMPLEMENTS; ENTITY DISAMBIGUATION; ENTITY RESOLUTIONS; GRAPH KERNELS; LOCAL NEIGHBORHOOD STRUCTURES; REAL-WORLD DATASETS; STATE-OF-THE-ART METHODS;

KNOWLEDGE MANAGEMENT; SUPPORT VECTOR MACHINES;

GRAPH THEORY;

EID: 84889595865     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2505515.2505565     Document Type: Conference Paper

References (32)

1. C. C. Aggarwal and S. Y. Philip. A condensation approach to privacy preserving data mining. Springer, 2004.
2. F. R. Bach. Graph kernels between point clouds. In Proc. of ICML, 2008.
3. R. Bekkerman and A. McCallum. Disambiguating web appearances of people in a social network. In Proc. of WWW, 2005.
4. I. Bhattacharya and L. Getoor. Iterative record linkage for cleaning and integration. In Proc. of DMKD, 2004.
5. I. Bhattacharya and L. Getoor. Entity resolutions in graphs. In Mining Graph Data. Wiley, 2006.
6. I. Bhattacharya and L. Getoor. A latent dirichlet model for unsupervised entity resolution. In Proc. of SDM, 2006.
7. C. Böhm, G. de Melo, F. Naumann, and G. Weikum. Linda: distributed web-of-data-scale entity matching. In Proc. of CIKM, 2012.
8. K. M. Borgwardt and H.-P. Kriegel. Shortest-path kernels on graphs. In Prof. of ICDM, 2005.
9. K. M. Borgwardt, C. S. Ong, S. Schönauer, S. Vishwanathan, A. J. Smola, and H.-P. Kriegel. Protein function prediction via graph kernels. Bioinformatics, 21(suppl 1):147-156, 2005.
10. J. Brewer. Kronecker products and matrix calculus in system theory. Circuits and Systems, IEEE Trans. on, 25(9):772-781, 1978.
11. R. Bunescu and M. Pasca. Using encyclopedic knowledge for named entity disambiguation. In Proc. of EACL, 2006.
12. S. Cucerzan. Large-scale named entity disambiguation based on wikipedia data. In Proc. of EMNLP-CoNLL, 2007.
13. C. P. Diehl, L. Getoor, and G. Namata. Name reference resolution in organizational email archives. In Prof. of SDM, 2006.
14. C. Dwork. Differential privacy: A survey of results. In Proc. of TAMC. 2008.
15. T. Elsayed, D. W. Oard, and G. Namata. Resolving personal names in email using context expansion. In Proc. of ACL, 2008.
16. R. W. Floyd. Algorithm 97: shortest path. Communications of the ACM, 5(6):345, 1962.
17. T. Gärtner, P. Flach, and S. Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129-143, 2003.
18. U. Kang, H. Tong, and J. Sun. Fast random walk graph kernel. In Proc. of SDM, 2012.
19. R. I. Kondor and J. Lafferty. Diffusion kernels on graphs and other discrete structures. In Proc. of ICML, 2002.
20. K. Liu, K. Das, T. Grandison, and H. Kargupta. Privacy-preserving data analysis on graphs and social networks. In H. Kargupta, J. Han, P.S. Yu, R. Motwani, and V. Kumar, editors, Next Generation of Data Mining. Chapman and Hall/CRC, 2008.
21. Y. Low, D. Bickson, J. Gonzalez, C. Guestrin, A. Kyrola, and J. M. Hellerstein. Distributed graphlab: a framework for machine learning and data mining in the cloud. Proc. of VLDB, 2012.
22. P. Mahé, N. Ueda, T. Akutsu, J.-L. Perret, and J.-P. Vert. Extensions of marginalized graph kernels. In Proc. of ICML, 2004.
23. B. Malin. Unsupervised name disambiguation via social network similarity. In Workshop on link analysis, counterterrorism, and security, Volume 1401, pages 93-102, 2005.
24. V. Rastogi, N. N. Dalvi, and M. N. Garofalakis. Large-scale collective entity matching. PVLDB, 4(4):208-218, 2011.
25. G. Salton and M. J. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA, 1986.
26. B. Schölkopf and A. J. Smola. Learning with kernels: Support vector machines, regularization, optimization, and beyond. MIT press, 2001.
27. P. Sen. Collective context-aware topic models for entity disambiguation. In Proc. of WWW, 2012.
28. S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter. Pegasos: primal estimated sub-gradient solver for SVM. Mathematical Programming, 127(1):3-30, 2011.
29. N. Shervashidze and K. M. Borgwardt. Fast subtree kernels on graphs. In Y. Bengio, D. Schuurmans, J.D. Lafferty, C. K. I. Williams, and A. Culotta, editors, NIPS, pages 1660-1668. Curran Associates, Inc., 2009.
30. N. Shervashidze, P. Schweitzer, E. J. van Leeuwen, K. Mehlhorn, and K. M. Borgwardt. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research, 12:2539-2561, 2011.
31. N. Shervashidze, S. Vishwanathan, T. Petri, K. Mehlhorn, and K. M. Borgwardt. Efficient graphlet kernels for large graph comparison. In Proc. of AISTATS, 2009.
32. S. Vishwanathan, N. N. Schraudolph, R. Kondor, and K. M. Borgwardt. Graph kernels. JMLR, 2010.