Information foraging through clustering and summarization: A self-organizing approach

Persistent Link:
http://hdl.handle.net/10150/283930
Title:
Information foraging through clustering and summarization: A self-organizing approach
Author:
Roussinov, Dmitri
Issue Date:
1999
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Successful knowledge management requires efficient tools to manage information in the form of text. However, our productivity in generating information has exceeded our ability to process it, and the dream of creating an information-rich society has become a nightmare of information overload. Although researchers and developers believe that interactive information access systems based on clustering and summarization offer a potential remedy to that problem, there is as yet no empirical evidence showing superiority of those tools over traditional keyword search. This dissertation attempted to determine whether automated clustering can help to find relevant information by suggesting an innovative implementation and verifying its potential ability to be of help. Our implementation is based on Kohonen's self-organizing maps and acts as a visualization layer between the user and a keyword-based search engine. We used the clustering properties of self-organizing maps to create a summary of search results. The user relies on this summary when deciding whether and how to provide additional feedback to the system to obtain more relevant documents. We have resolved multiple issues related to the speed and quality of output associated with self-organizing maps and created a version (Adaptive Search) that allows interactive Internet searching. We have performed user studies and a controlled experiment in order to test the proposed approach. In a laboratory experiment, subjects spent less time finding correct answers using Adaptive Search than using the search engine directly. In addition, the documents containing answers were positioned consistently higher in the rank-ordered lists suggested by Adaptive Search as opposed to the lists suggested by the search engine. The search engine that we used was AltaVista, known to be one of the most popular, comprehensive and flexible engines on the Web. Our main conclusion is that indeed information clustering helps information seekers if properly implemented.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Library Science.; Information Science.; Computer Science.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Industrial Management
Degree Grantor:
University of Arizona
Advisor:
Chen, Hsinchun

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleInformation foraging through clustering and summarization: A self-organizing approachen_US
dc.creatorRoussinov, Dmitrien_US
dc.contributor.authorRoussinov, Dmitrien_US
dc.date.issued1999en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractSuccessful knowledge management requires efficient tools to manage information in the form of text. However, our productivity in generating information has exceeded our ability to process it, and the dream of creating an information-rich society has become a nightmare of information overload. Although researchers and developers believe that interactive information access systems based on clustering and summarization offer a potential remedy to that problem, there is as yet no empirical evidence showing superiority of those tools over traditional keyword search. This dissertation attempted to determine whether automated clustering can help to find relevant information by suggesting an innovative implementation and verifying its potential ability to be of help. Our implementation is based on Kohonen's self-organizing maps and acts as a visualization layer between the user and a keyword-based search engine. We used the clustering properties of self-organizing maps to create a summary of search results. The user relies on this summary when deciding whether and how to provide additional feedback to the system to obtain more relevant documents. We have resolved multiple issues related to the speed and quality of output associated with self-organizing maps and created a version (Adaptive Search) that allows interactive Internet searching. We have performed user studies and a controlled experiment in order to test the proposed approach. In a laboratory experiment, subjects spent less time finding correct answers using Adaptive Search than using the search engine directly. In addition, the documents containing answers were positioned consistently higher in the rank-ordered lists suggested by Adaptive Search as opposed to the lists suggested by the search engine. The search engine that we used was AltaVista, known to be one of the most popular, comprehensive and flexible engines on the Web. Our main conclusion is that indeed information clustering helps information seekers if properly implemented.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectLibrary Science.en_US
dc.subjectInformation Science.en_US
dc.subjectComputer Science.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineIndustrial Managementen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorChen, Hsinchunen_US
dc.identifier.proquest9946848en_US
dc.identifier.bibrecord.b39918038en_US
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