Skip to main content

Analysis of Implicit Relations on Wikipedia: Measuring Strength through Mining Elucidatory Objects

  • Conference paper
Book cover Database Systems for Advanced Applications (DASFAA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5981))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Koren, Y., North, S.C., Volinsky, C.: Measuring and extracting proximity in networks. In: Proc. of 12th ACM SIGKDD Conference, pp. 245–255 (2006)

    Google Scholar 

  2. 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)

    Google Scholar 

  3. 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)

    Chapter  Google Scholar 

  4. Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms, and Applications. Prentice Hall, New Jersey (1993)

    Google Scholar 

  5. Wayne, K.D.: Generalized Maximum Flow Algorithm. PhD thesis, Cornell University, New York, U.S. (January 1999)

    Google Scholar 

  6. Cilibrasi, R.L., Vitányi, P.M.B.: The Google similarity distance. IEEE Transactions on Knowledge and Data Engineering 19(3), 370–383 (2007)

    Article  Google Scholar 

  7. 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)

    Google Scholar 

  8. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: a core of semantic knowledge. In: Proc. of 16th WWW, pp. 697–706 (2007)

    Google Scholar 

  9. Erdös Number: The Erdös number project, http://www.oakland.edu/enp/

  10. 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)

    Article  Google Scholar 

  11. White, H.D., Griffith, B.C.: Author cocitation: A literature measure of intellectual structure. JASIST 32(3), 163–171 (1981)

    Article  Google Scholar 

  12. Milne, D., Witten, I.H.: An effective, low-cost measure of semantic relatedness obtained from wikipedia links (2008)

    Google Scholar 

  13. Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: Proc. of 8th ACM SIGKDD Conference, pp. 538–543 (2002)

    Google Scholar 

  14. Hubbell, C.H.: An input-output approach to clique identification. Sociolmetry 28, 277–299 (1965)

    Google Scholar 

  15. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    Article  MATH  Google Scholar 

  16. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Application (Structural Analysis in the Social Sciences). Cambridge University Press, New York (1994)

    Google Scholar 

  17. Faloutsos, C., Mccurley, K.S., Tomkins, A.: Fast discovery of connection subgraphs. In: Proc. of 10th ACM SIGKDD Conference, pp. 118–127 (2004)

    Google Scholar 

  18. Doyle, P.G., Snell, J.L.: Random Walks and Electric Networks, vol. 22. Mathematical Association America, New York (1984)

    MATH  Google Scholar 

  19. Tong, H., Faloutsos, C.: Center-piece subgraphs: Problem definition and fast solutions. In: Proc. of 12th ACM SIGKDD Conference, pp. 404–413 (2006)

    Google Scholar 

  20. 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)

    Google Scholar 

  21. 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)

    Google Scholar 

  22. 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)

    Chapter  Google Scholar 

  23. Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E.: The WordSimilarity-353 Test Collection (2002)

    Google Scholar 

  24. Coutsoukis, P.: Country ranks (2009), http://www.photius.com/rankings/index.html

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-12026-8_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12025-1

  • Online ISBN: 978-3-642-12026-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics