Persistent Link:
http://hdl.handle.net/10150/105775
Title:
Knowledge-Based Document Retrieval: Framework and Design
Author:
Chen, Hsinchun
Citation:
Knowledge-Based Document Retrieval: Framework and Design 1992-06, 18(3):293-314 Journal of Information Science: Principles and Practice
Journal:
Journal of Information Science: Principles and Practice
Issue Date:
Jun-1992
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105775
Submitted date:
2004-10-01
Abstract:
This article presents research on the design of knowledge-based document retrieval systems. We adopted a semantic network structure to represent subject knowledge and classification scheme knowledge and modeled experts' search strategies and user modeling capability as procedural knowledge. These functionalities were incorporated into a prototype knowledge-based retrieval system, Metacat. Our system, the design of which was based on the blackboard architecture, was able to create a user profile, identify task requirements, suggest heuristics-based search strategies, perform semantic-based search assistance, and assist online query refinement.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Artificial Intelligence; Information Extraction
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab; Information retrieval

Full metadata record

DC FieldValue Language
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-10-01T00:00:01Z-
dc.date.available2010-06-18T23:34:10Z-
dc.date.issued1992-06en_US
dc.date.submitted2004-10-01en_US
dc.identifier.citationKnowledge-Based Document Retrieval: Framework and Design 1992-06, 18(3):293-314 Journal of Information Science: Principles and Practiceen_US
dc.identifier.urihttp://hdl.handle.net/10150/105775-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractThis article presents research on the design of knowledge-based document retrieval systems. We adopted a semantic network structure to represent subject knowledge and classification scheme knowledge and modeled experts' search strategies and user modeling capability as procedural knowledge. These functionalities were incorporated into a prototype knowledge-based retrieval system, Metacat. Our system, the design of which was based on the blackboard architecture, was able to create a user profile, identify task requirements, suggest heuristics-based search strategies, perform semantic-based search assistance, and assist online query refinement.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectInformation Extractionen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherInformation retrievalen_US
dc.titleKnowledge-Based Document Retrieval: Framework and Designen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of Information Science: Principles and Practiceen_US
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