Knowledge-Based Document Retrieval: Framework and Design
dc.contributor.author | Chen, Hsinchun | |
dc.date.accessioned | 2004-10-01T00:00:01Z | |
dc.date.available | 2010-06-18T23:34:10Z | |
dc.date.issued | 1992-06 | en_US |
dc.date.submitted | 2004-10-01 | en_US |
dc.identifier.citation | Knowledge-Based Document Retrieval: Framework and Design 1992-06, 18(3):293-314 Journal of Information Science: Principles and Practice | en_US |
dc.identifier.uri | http://hdl.handle.net/10150/105775 | |
dc.description | Artificial Intelligence Lab, Department of MIS, University of Arizona | en_US |
dc.description.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. | |
dc.format.mimetype | application/pdf | en_US |
dc.language.iso | en | en_US |
dc.subject | Artificial Intelligence | en_US |
dc.subject | Information Extraction | en_US |
dc.subject.other | National Science Digital Library | en_US |
dc.subject.other | NSDL | en_US |
dc.subject.other | Artificial intelligence lab | en_US |
dc.subject.other | AI lab | en_US |
dc.subject.other | Information retrieval | en_US |
dc.title | Knowledge-Based Document Retrieval: Framework and Design | en_US |
dc.type | Journal Article (Paginated) | en_US |
dc.identifier.journal | Journal of Information Science: Principles and Practice | en_US |
refterms.dateFOA | 2018-06-12T02:46:59Z | |
html.description.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. |