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Invited Talk
ARCH: An Adaptive Agent
for Retrieval Based on Concept Hierarchies
Abstract:
We present a client-side agent, named ARCH, for assisting users in one
of the most difficult information retrieval tasks, i.e., that of formulating
an effective search query. The agent utilizes a hierarchically-organized
semantic knowledge base in aggregate form, as well as an automatically
learned user profile, to enhance user queries. In contrast to traditional
methods based on relevance feedback, ARCH assists users in query modification
prior to the search task. The initial user query is (semi-)automatically
modified based on the user's interaction with an embedded, but modular,
concept hierarchy. The modular design of the agent allows users to switch
among the representations of different domain-specific hierarchies depending
on the goals of the search. ARCH passively learns a user profile by
observing the user's past browsing behavior. The profiles are used to
provide additional context to the user's information need represented
by the initial query. The full system also incorporates mechanisms for
categorizing and filtering the search results, and using these categories
for performing refined searches in the background. Preliminary experiments
have shown that the agent can substantially improve the effectiveness
of information retrieval both in the general context of the Web, as
well as for search against domain-specific document indexes.
Bamshad
Mobasher
Director, Center
for Web Intelligence School of Computer Science, Telecommunication,
and Information Systems DePaul University Chicago, Illinois, USA
Accepted papers
- Acquiring Conceputal Relationships
from a MRD and Text Corpus,
M. Kurematsu, N. Nakaya, T. Yamaguchi
- Multiagent Cooperative
Learning of User Preferences,
A. Kiss, J. Quinqueton
- Ontology Discovery for
the Semantic Web Using Hierarchical Clustering,
P. Clerkin, P. Cunningham, C. Hayes
- Semantic Web Mining Workshop,
R.H.P. Engels, B.A. Bremdal, R. Jones
- Utilising an Ontology
Based Repository to Connect Web Miners and Application Agents,
S. Haustein
- Web Directories as Training
Data for Automated Metadata Extraction,
M. Kavalec, V. Svátek, P. Strossa
- XML Topic Maps and Semantic
Web Mining,
B. Le Grand, M. Soto
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