Distributed Hypertext Resource Discovery Through Examples.
Soumen Chakrabarti, Martin van den Berg, Byron Dom:
Distributed Hypertext Resource Discovery Through Examples.
VLDB 1999: 375-386@inproceedings{DBLP:conf/vldb/ChakrabartiBD99,
author = {Soumen Chakrabarti and
Martin van den Berg and
Byron Dom},
editor = {Malcolm P. Atkinson and
Maria E. Orlowska and
Patrick Valduriez and
Stanley B. Zdonik and
Michael L. Brodie},
title = {Distributed Hypertext Resource Discovery Through Examples},
booktitle = {VLDB'99, Proceedings of 25th International Conference on Very
Large Data Bases, September 7-10, 1999, Edinburgh, Scotland,
UK},
publisher = {Morgan Kaufmann},
year = {1999},
isbn = {1-55860-615-7},
pages = {375-386},
ee = {db/conf/vldb/ChakrabartiBD99.html},
crossref = {DBLP:conf/vldb/99},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
We describe the architecture of a hypertext resource discovery system using a
relational database. Such a system can answer questions that combine page contents,
meta-data, and hyperlink structure in powerful ways, such as "find the number of links
from an environmental protection page to a page about oil and natural gas over the
last year." A key problem in populating the database in such a system is to discover
web resources related to the topics involved in such queries. We argue that a
keyword-based "find similar" search based on a giant all-purpose crawler is neither
necessary nor adequate for resource discovery. Instead we exploit the properties that
pages tend to cite pages with related topics, and given that a page u cites a
page about a desired topic, it is very likely that u cites additional desirable
pages. We exploit these properties by using a crawler controlled by two hypertext
mining programs: (1) a classifier that evaluates the relevance of a region of the web
to the user's interest (2) a distiller that evaluates a page as an access point for a
large neighborhood of relevant pages. Our implementation uses IBM's Universal Database,
not only for robust data storage, but also for integrating the computations of the
classifier and distiller into the database. This results in significant increase in
I/O efficiency: a factor of ten for the classifier and a factor of three for the
distiller. In addition, ad-hoc SQL queries can be used to monitor the crawler, and
dynamically change crawling strategies. We report on experiments to establish that
our system is efficient, effective, and robust.
Copyright © 1999 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
DVD Version: Load ACM SIGMOD Anthology DVD 1" and ...
BibTeX
Printed Edition
Malcolm P. Atkinson, Maria E. Orlowska, Patrick Valduriez, Stanley B. Zdonik, Michael L. Brodie (Eds.):
VLDB'99, Proceedings of 25th International Conference on Very Large Data Bases, September 7-10, 1999, Edinburgh, Scotland, UK.
Morgan Kaufmann 1999, ISBN 1-55860-615-7
Contents BibTeX
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BibTeX
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