ACM SIGMOD Anthology VLDB dblp.uni-trier.de

Using Taxonomy, Discriminants, and Signatures for Navigating in Text Databases.

Soumen Chakrabarti, Byron Dom, Rakesh Agrawal, Prabhakar Raghavan: Using Taxonomy, Discriminants, and Signatures for Navigating in Text Databases. VLDB 1997: 446-455
@inproceedings{DBLP:conf/vldb/ChakrabartiDAR97,
  author    = {Soumen Chakrabarti and
               Byron Dom and
               Rakesh Agrawal and
               Prabhakar Raghavan},
  editor    = {Matthias Jarke and
               Michael J. Carey and
               Klaus R. Dittrich and
               Frederick H. Lochovsky and
               Pericles Loucopoulos and
               Manfred A. Jeusfeld},
  title     = {Using Taxonomy, Discriminants, and Signatures for Navigating
               in Text Databases},
  booktitle = {VLDB'97, Proceedings of 23rd International Conference on Very
               Large Data Bases, August 25-29, 1997, Athens, Greece},
  publisher = {Morgan Kaufmann},
  year      = {1997},
  isbn      = {1-55860-470-7},
  pages     = {446-455},
  ee        = {db/conf/vldb/ChakrabartiDAR97.html},
  crossref  = {DBLP:conf/vldb/97},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

We explore how to organize a text database hierarchically to aid better searching and browsing. We propose to exploit the natural hierarchy of topics, or taxonomy, that many corpora, such as internet directories, digital libraries, and patent databases enjoy. In our system, the user navigates through the query response not as a flat unstructured list, but embedded in the familiar taxonomy , and annotated with document signatures computed dynamically with respect to where the user is located at any time. We show how to update such databases with new documents with high speed and accuracy. We use techniques from statistical pattern recognition to effciently separate the feature words or discriminants from the noise words at each node of the taxonomy. Using these, we build a multi-level classifier. At each node, this classifier can ignore the large number of noise words in a documen t. Thus the classifier has a small model size and is very fast. However, owing to the use of context-sensitive features, the classifier is very accurate. We report on experiences with the Reuters newswire benchmark, the US Patent database, and web document samples from Yahoo!.

Copyright © 1997 by the VLDB Endowment. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by the permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and/or special permission from the Endowment.

Online Paper

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Printed Edition

Matthias Jarke, Michael J. Carey, Klaus R. Dittrich, Frederick H. Lochovsky, Pericles Loucopoulos, Manfred A. Jeusfeld (Eds.): VLDB'97, Proceedings of 23rd International Conference on Very Large Data Bases, August 25-29, 1997, Athens, Greece. Morgan Kaufmann 1997, ISBN 1-55860-470-7
Contents BibTeX

Electronic Edition

From CS Dept., University Trier (Germany)

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Referenced by

  1. Rakesh Agrawal, Roberto J. Bayardo Jr., Ramakrishnan Srikant: Athena: Mining-Based Interactive Management of Text Database. EDBT 2000: 365-379
  2. Ke Wang, Senqiang Zhou, Shiang Chen Liew: Building Hierarchical Classifiers Using Class Proximity. VLDB 1999: 363-374
  3. 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)
  4. Soumen Chakrabarti, Byron Dom, Piotr Indyk: Enhanced Hypertext Categorization Using Hyperlinks. SIGMOD Conference 1998: 307-318
BibTeX
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