Link-based and content-based evidential information in a belief network model

ABSTRACT

This work presents an information retrieval model developed to deal with hyperlinked environments. The model is based on belief networks and provides a framework for combining information extracted from the content of the documents with information derived from cross-references among the documents. The information extracted from the content of the documents is based on statistics regarding the keywords in the collection and is one of the basis for traditional information retrieval (IR) ranking algorithms. The information derived from cross-references among the documents is based on link references in a hyperlinked environment and has received increased attention lately due to the success of the Web. We discuss a set of strategies for combining these two types of sources of evidential information and experiment with them using a reference collection extracted from the Web. The results show that this type of combination can improve the retrieval performance without requiring any extra information from the users at query time. In our experiments, the improvements reach up to 59% in terms of average precision figures.

REFERENCES

• 1: Ricardo A. Baeza-Yates , Berthier Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1999
• 2: Krishna Bharat , Monika R. Henzinger, Improved algorithms for topic distillation in a hyperlinked environment, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, p.104-111, August 24-28, 1998, Melbourne, Australia [doi>10.1145/290941.290972]
• 3: J. Bichteler and E. A. Eaton III. The combined use of bibliographic coupling and cocitation for document retrieval. Journal of the American Society for Information Science, 31(7):278-282, 1980.
• 4: Sergey Brin , Lawrence Page, The anatomy of a large-scale hypertextual Web search engine, Proceedings of the seventh international conference on World Wide Web 7, p.107-117, April 1998, Brisbane, Australia
• 5: Soumen Chakrabarti , Byron Dom , Prabhakar Raghavan , Sridhar Rajagopalan , David Gibson , Jon Kleinberg, Automatic resource compilation by analyzing hyperlink structure and associated text, Proceedings of the seventh international conference on World Wide Web 7, p.65-74, April 1998, Brisbane, Australia
• 6: Altigran S. da Silva , Eveline A. Veloso , Paulo B. Golghe , Berthier Ribeiro-Neto , Alberto H. F. Laender , Nivio Ziviani, CoBWeb A Crawler for the Brazilian Web, Proceedings of the String Processing and Information Retrieval Symposium & International Workshop on Groupware, p.184, September 21-24, 1999
• 7: William B. Frakes , Ricardo Baeza-Yates, Information retrieval: data structures and algorithms, Prentice-Hall, Inc., Upper Saddle River, NJ, 1992
• 8: A. Grifliths, H. C. Luckhurst, and P. Willett. Using interdocument similarity information in document retrieval systems. Journal of the American Society for Information Science, 37:3-11, 1986.
• 9: D. Hawking, N. Craswell, and P. Thistlewaste. Overview of trec-7 very large collection track. In Proc. of the Seventh Text Retrieval Conference - (TREC-7), pages 91-104, Gaithersburg, Maryland, 1998. National Institute of Standards and Technology.
• 10: David Hawking , Nick Craswell , Paul Thistlewaite , Donna Harman, Results and challenges in Web search evaluation, Proceeding of the eighth international conference on World Wide Web, p.1321-1330, May 1999, Toronto, Canada
• 11: Bernard J. Jansen , Amanda Spink , Judy Bateman , Tefko Saracevic, Real life information retrieval: a study of user queries on the Web, ACM SIGIR Forum, v.32 n.1, p.5-17, Spring 1998 [doi>10.1145/281250.281253]
• 12: Jon M. Kleinberg, Authoritative sources in a hyperlinked environment, Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms, p.668-677, January 25-27, 1998, San Francisco, California, United States
• 13: Judea Pearl, Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1988
• 14: B. Ribeiro-Neto, I. Silva, and R. Muntz. Bayesian etwork models for ir. In: Soft Computing in Infor. marion Retrieval: Techniques and Applications, F. Crestani and G. Pasi editors, Springer Verlag. To appear.
• 15: Berthier A. N. Ribeiro , Richard Muntz, A belief network model for IR, Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, p.253-260, August 18-22, 1996, Zurich, Switzerland [doi>10.1145/243199.243272]
• 16: Gerard Salton , Michael J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, Inc., New York, NY, 1986
• 17: Gerard. Salton, Automatic Information Organization and Retrieval., McGraw Hill Text, 1968
• 18: H. G. Small and M. E. D. Koenig. Journal clustering using a bibliographic coupling method. Information Processing 14 Management, 13:277-288, 1977.
• 19: Howard Turtle , W. Bruce Croft, Evaluation of an inference network-based retrieval model, ACM Transactions on Information Systems (TOIS), v.9 n.3, p.187-222, July 1991 [doi>10.1145/125187.125188]
• 20: Ian H. Witten , Alistair Moffat , Timothy C. Bell, Managing gigabytes (2nd ed.): compressing and indexing documents and images, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1999
• 21: S. K. M. Wong , Y. Y. Yao, A probabilistic inference model for information retrieval, Information Systems, v.16 n.3, p.301-321, 1991 [doi>10.1016/0306-4379(91)90003-R]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL

Additional Classification:
  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS
      G.2.2 Graph Theory
          Subjects: Network problems

  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)

General Terms:
Algorithms, Design, Performance, Reliability

Keywords:
IR models, content-based retrieval, exploiting hyperlinked structure