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Enhanced Hypertext Categorization Using Hyperlinks.

Soumen Chakrabarti, Byron Dom, Piotr Indyk: Enhanced Hypertext Categorization Using Hyperlinks. SIGMOD Conference 1998: 307-318
@inproceedings{DBLP:conf/sigmod/ChakrabartiDI98,
  author    = {Soumen Chakrabarti and
               Byron Dom and
               Piotr Indyk},
  editor    = {Laura M. Haas and
               Ashutosh Tiwary},
  title     = {Enhanced Hypertext Categorization Using Hyperlinks},
  booktitle = {SIGMOD 1998, Proceedings ACM SIGMOD International Conference
               on Management of Data, June 2-4, 1998, Seattle, Washington, USA},
  publisher = {ACM Press},
  year      = {1998},
  isbn      = {0-89791-995-5},
  pages     = {307-318},
  ee        = {http://doi.acm.org/10.1145/276304.276332, db/conf/sigmod/ChakrabartiDI98.html},
  crossref  = {DBLP:conf/sigmod/98},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

A major challenge in indexing unstructured hypertext databases is to automatically extract meta-data that enables structured search using topic taxonomies, circumvents keyword ambiguity, and improves the quality of search and profile-based routing and filtering. Therefore, an accurate classifier is an essential component of a hypertext database. Hyperlinks pose new problems not addressed in the extensive text classification literature. Links clearly contain high-quality semantic clues that are lost upon a purely term-based classifier, but exploiting link information is non-trivial because it is noisy. Naive use of terms in the link neighborhood of a document can even degrade accuracy. Our contribution is to propose robust statistical models and a relaxation labeling technique for better classification by exploiting link information in a small neighborhood around documents. Our technique also adapts gracefully to the fraction of neighboring documents having known topics. We experimented with pre-classified samples from Yahoo! and the US Patent Database. In previous work, we developed a text classifier that misclassified only 13% of the documents in the well-known Reuters benchmark; this was comparable to the best results ever obtained. This classifier misclassified 36% of the patents, indicating that classifying hypertext can be more difficult than classifying text. Naively using terms in neighboring documents increased error to 38%; our hypertext classifier reduced it to 21%. Results with the Yahoo! sample were more dramatic: the text classifier showed 68% error, whereas our hypertext classifier reduced this to only 21%.

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Laura M. Haas, Ashutosh Tiwary (Eds.): SIGMOD 1998, Proceedings ACM SIGMOD International Conference on Management of Data, June 2-4, 1998, Seattle, Washington, USA. ACM Press 1998, ISBN 0-89791-995-5 BibTeX , SIGMOD Record 27(2), June 1998
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[Abstract]
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Referenced by

  1. Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, D. Sivakumar, Andrew Tomkins, Eli Upfal: The Web as a Graph. PODS 2000: 1-10
  2. Minos N. Garofalakis, Rajeev Rastogi, S. Seshadri, Kyuseok Shim: Data Mining and the Web: Past, Present and Future. Workshop on Web Information and Data Management 1999: 43-47
  3. Ke Wang, Senqiang Zhou, Shiang Chen Liew: Building Hierarchical Classifiers Using Class Proximity. VLDB 1999: 363-374
  4. Soumen Chakrabarti, Martin van den Berg, Byron Dom: Distributed Hypertext Resource Discovery Through Examples. VLDB 1999: 375-386
  5. Soumen Chakrabarti, Byron Dom, Rakesh Agrawal, Prabhakar Raghavan: Scalable Feature Selection, Classification and Signature Generation for Organizing Large Text Databases into Hierarchical Topic Taxonomies. VLDB J. 7(3): 163-178(1998)
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