Skip to main content
SpringerLink
Log in

Algorithmic Computation and Approximation of Semantic Similarity

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Automatic extraction of semantic information from text and links in Web pages is key to improving the quality of search results. However, the assessment of automatic semantic measures is limited by the coverage of user studies, which do not scale with the size, heterogeneity, and growth of the Web. Here we propose to leverage human-generated metadata—namely topical directories—to measure semantic relationships among massive numbers of pairs of Web pages or topics. The Open Directory Project classifies millions of URLs in a topical ontology, providing a rich source from which semantic relationships between Web pages can be derived. While semantic similarity measures based on taxonomies (trees) are well studied, the design of well-founded similarity measures for objects stored in the nodes of arbitrary ontologies (graphs) is an open problem. This paper defines an information-theoretic measure of semantic similarity that exploits both the hierarchical and non-hierarchical structure of an ontology. An experimental study shows that this measure improves significantly on the traditional taxonomy-based approach. This novel measure allows us to address the general question of how text and link analyses can be combined to derive measures of relevance that are in good agreement with semantic similarity. Surprisingly, the traditional use of text similarity turns out to be ineffective for relevance ranking.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Comput. Networks 30(1–7), 107–117 (1998)

    Google Scholar 

  2. Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. MIT Press, Cambridge, MA (1990)

  3. Cover, T., Thomas., J.: Elements of Information Theory. Wiley & Sons, Inc., New York, NY (1991)

  4. Cristianini, N., Shawe-Taylor, J., Lodhi, H.: Latent semantic kernels. In: Brodley, C., Danyluk, A. (eds.) Proceedings of ICML-01, 18th International Conference on Machine Learning, pp. 66–73, Williams College, USA, 2001. Morgan Kaufmann, San Francisco (2001)

  5. Deerwester, S.C., Dumais, S.T., Landauer, T.K., Furnas, G.W., Harshman, R.A.: Indexing by latent semantic analysis. J. Am. Soc. Inf. Sci. 41(6), 391–407 (1990)

    Article  Google Scholar 

  6. Fox, C.: Lexical analysis and stop lists. In: Information Retrieval: Data Structures and Algorithms. Prentice-Hall, Englewood Cliffs, NJ (1992)

  7. Ganesan, P., Garcia-Molina, H., Widom, J.: Exploiting hierarchical domain structure to compute similarity. ACM Trans. Inf. Syst. 21(1), 64–93 (2003)

    Article  Google Scholar 

  8. Gruber, T.R.: A translation approach to portable ontologies. Knowl. Acquis. 5(2), 199–220 (1993)

    Article  Google Scholar 

  9. Haveliwala, T., Gionis, A., Klein, D., Indyk, P.: Evaluating strategies for similarity search on the Web. In: Lassner, D., De Roure, D., Iyengar, A. (eds.) Proc. 11th International World Wide Web Conference, New York, New York, 2002. ACM (2002)

  10. Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of the International Conference on Research in Computational Linguistics (ROCLING X), Taiwan (1998)

  11. Joslyn, C., Bruno, W.J.: Weighted pseudo-distances for categorization in semantic hierarchies. In: International Conference on Conceptual Structures, Kassel, Germany. Lecture Notes in Computer Science 3956, pp. 381–395 (2005)

  12. Kandel, A.: Fuzzy Mathematical Techniques with Applications. Addison-Wesley, Reading, MA (1986)

  13. Kandola, J., Shawe-Taylor, J., Cristianini, N.: Learning semantic similarity. In: Neural Information Processing Systems 15, pp. 657–664 (2002)

  14. Kessler, M.: Bibliographic coupling between scientific papers. Am. Doc. 14, 10–25 (1963)

    Google Scholar 

  15. Kleinberg, J.M., Tardos, E.: Approximation algorithms for classification problems with pairwise relationships: metric labeling and Markov random fields. In: IEEE Symposium on Foundations of Computer Science, New York, NY, pp. 14–23 (1999)

  16. Korfhage, R.: Information Storage and Retrieval. Wiley, New York, New York (1997)

    Google Scholar 

  17. Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the Fifteenth International Conference on Machine Learning, Madison, WI, pp. 296–304 (1998)

  18. Liu, N., Zhang, B., Yan, J., Yang, Q., Yan, S., Chen, Z., Bai, F., Ma, W.-Y.: Learning similarity measures in non-orthogonal space. In: CIKM ‘04: Proceedings of the thirteenth ACM conference on Information and knowledge management, pp. 334–341, New York, New York, 2004. ACM (2004)

  19. Lord, P.W., Stevens, R.D., Brass, A., Goble, C.A.: Investigating semantic similarity measures across the gene ontology: the relationship between sequence and annotation. Bioinformatics 19(10), 1275–1283 (2003)

    Article  Google Scholar 

  20. Lu, W., Janssen, J., Milios, E., Japkowicz, N.: Node similarity in networked information spaces. In: Proceedings of the Conference of the IBM Centre for Advanced Studies on Collaborative Research (CASCONF01), Toronto, Ontario, Canada, IBM (2001)

  21. Menczer, F.: Combining link and content analysis to estimate semantic similarity. In: Alt. Track Papers and Posters Proc. 13th International World Wide Web Conference, New York, NY, pp. 452–453 (2004)

  22. Menczer, F.: Correlated topologies in citation networks and the web. Eur. Phys. J. B 38(2), 211–221 (2004)

    Article  Google Scholar 

  23. Menczer, F.: Mapping the semantics of web text and links. IEEE Internet Computing 9(3):27–36 (2005), May/June

    Article  Google Scholar 

  24. Montes-y-Gómez, M., Gelbukh, A., López-López, A., Baeza-Yates, R.: Flexible comparison of conceptual graphs. In: Proc. 12th International Conference and Workshop on Database and Expert Systems Applications (DEXA), Lecture Notes in Computer Science. Springer, Berlin Heidelberg New York (2001)

  25. Pedersen, T., Patwardhan, S., Michelizzo, J.: Wordnet: similarity-measuring the relatedness of concepts. In: Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI-04), San Jose, CA, pp. 1024–1025 (2004)

  26. Polalck, S.: Measures for the comparison of information retrieval systems. Am. Doc. 19(4), 387–397 (1968)

    Google Scholar 

  27. Porter, M.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)

    Google Scholar 

  28. Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern. 19(1), 17–30 (1989)

    Article  Google Scholar 

  29. Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95), Montreal, Canada, pp. 448–453 (1995)

  30. Salton, G., McGill, M.: An Introduction to Modern Information Retrieval. McGraw-Hill, New York, New York (1983)

    Google Scholar 

  31. Small, H.: Co-citation in the scientific literature: a new measure of the relationship between documents. J. Am. Soc. Inf. Sci. 42, 676–684 (1973)

    Google Scholar 

  32. Tversky, A.: Features of similarity. Psychol. Rev. 84(4), 327–352 (1977)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Indiana University, Bloomington, IN, 47408, USA

    Ana G. Maguitman, Filippo Menczer, Fulya Erdinc & Heather Roinestad

  2. School of Infomatics, Indiana University, Eigenmann Hall 909, 1900 East Tenth Street, Bloomington, IN, 47408, USA

    Ana G. Maguitman, Filippo Menczer & Alessandro Vespignani

Authors
  1. Ana G. Maguitman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Filippo Menczer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fulya Erdinc
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Heather Roinestad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alessandro Vespignani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Filippo Menczer.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Maguitman, A.G., Menczer, F., Erdinc, F. et al. Algorithmic Computation and Approximation of Semantic Similarity. World Wide Web 9, 431–456 (2006). https://doi.org/10.1007/s11280-006-8562-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-006-8562-2

Keywords

Search

Navigation