Abstract
We focus on measuring relations between pairs of objects in Wikipedia whose pages can be regarded as individual objects. Two kinds of relations between two objects exist: in Wikipedia, an explicit relation is represented by a single link between the two pages for the objects, and an implicit relation is represented by a link structure containing the two pages. Previously proposed methods are inadequate for measuring implicit relations because they use only one or two of the following three important factors: distance, connectivity, and co-citation. We propose a new method reflecting all the three factors by using a generalized maximum flow. We confirm that our method can measure the strength of a relation more appropriately than these previously proposed methods do. Another remarkable aspect of our method is mining elucidatory objects, that is, objects constituting a relation. We explain that mining elucidatory objects opens a novel way to deeply understand a relation.
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References
Koren, Y., North, S.C., Volinsky, C.: Measuring and extracting proximity in networks. In: Proc. of 12th ACM SIGKDD Conference, pp. 245–255 (2006)
Ito, M., Nakayama, K., Hara, T., Nishio, S.: Association thesaurus construction methods based on link co-occurrence analysis for wikipedia. In: CIKM, pp. 817–826 (2008)
Nakayama, K., Hara, T., Nishio, S.: Wikipedia mining for an association web thesaurus construction. In: Benatallah, B., Casati, F., Georgakopoulos, D., Bartolini, C., Sadiq, W., Godart, C. (eds.) WISE 2007. LNCS, vol. 4831, pp. 322–334. Springer, Heidelberg (2007)
Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms, and Applications. Prentice Hall, New Jersey (1993)
Wayne, K.D.: Generalized Maximum Flow Algorithm. PhD thesis, Cornell University, New York, U.S. (January 1999)
Cilibrasi, R.L., Vitányi, P.M.B.: The Google similarity distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370–383 (2007)
Kasneci, G., Suchanek, F.M., Ifrim, G., Ramanath, M., Weikum, G.: Naga: Searching and ranking knowledge. In: Proc. of 24th ICDE, pp. 953–962 (2008)
Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proc. of 16th WWW, pp. 697–706 (2007)
Erdös Number: The Erdös number project, http://www.oakland.edu/enp/
Lu, W., Janssen, J., Milios, E., Japkowicz, N., Zhang, Y.: Node similarity in the citation graph. Knowledge and Information Systems 11(1), 105–129 (2006)
White, H.D., Griffith, B.C.: Author cocitation: A literature measure of intellectual structure. JASIST 32(3), 163–171 (1981)
Milne, D., Witten, I.H.: An effective, low-cost measure of semantic relatedness obtained from wikipedia links (2008)
Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: Proc. of 8th ACM SIGKDD Conference, pp. 538–543 (2002)
Hubbell, C.H.: An input-output approach to clique identification. Sociolmetry 28, 277–299 (1965)
Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Application (Structural Analysis in the Social Sciences). Cambridge University Press, New York (1994)
Faloutsos, C., Mccurley, K.S., Tomkins, A.: Fast discovery of connection subgraphs. In: Proc. of 10th ACM SIGKDD Conference, pp. 118–127 (2004)
Doyle, P.G., Snell, J.L.: Random Walks and Electric Networks, vol. 22. Mathematical Association America, New York (1984)
Tong, H., Faloutsos, C.: Center-piece subgraphs: Problem definition and fast solutions. In: Proc. of 12th ACM SIGKDD Conference, pp. 404–413 (2006)
Zhu, J., Nie, Z., Liu, X., Zhang, B., Wen, J.R.: Statsnowball: a statistical approach to extracting entity relationships. In: WWW, pp. 101–110 (2009)
Xi, W., Fox, E.A., Fan, W., Zhang, B., Chen, Z., Yan, J., Zhuang, D.: Simfusion: measuring similarity using unified relationship matrix. In: Proc. of 28th SIGIR, pp. 130–137 (2005)
Gracia, J., Mena, E.: Web-based measure of semantic relatedness. In: Bailey, J., Maier, D., Schewe, K.-D., Thalheim, B., Wang, X.S. (eds.) WISE 2008. LNCS, vol. 5175, pp. 136–150. Springer, Heidelberg (2008)
Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E.: The WordSimilarity-353 Test Collection (2002)
Coutsoukis, P.: Country ranks (2009), http://www.photius.com/rankings/index.html
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Zhang, X., Asano, Y., Yoshikawa, M. (2010). Analysis of Implicit Relations on Wikipedia: Measuring Strength through Mining Elucidatory Objects. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 5981. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12026-8_35
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DOI: https://doi.org/10.1007/978-3-642-12026-8_35
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