A Machine Learning Approach to Inductive Query by Examples: An Experiment Using Relevance Feedback, ID3, Genetic Algorithms, and Simulated Annealing

Persistent Link:
http://hdl.handle.net/10150/106135
Title:
A Machine Learning Approach to Inductive Query by Examples: An Experiment Using Relevance Feedback, ID3, Genetic Algorithms, and Simulated Annealing
Author:
Chen, Hsinchun; Shankaranarayanan, Ganesan; She, Linlin; Iyer, Anand
Citation:
A Machine Learning Approach to Inductive Query by Examples: An Experiment Using Relevance Feedback, ID3, Genetic Algorithms, and Simulated Annealing 1998-06, 49(8):693-705 Journal of the American Society for Information Science
Publisher:
Wiley Periodicals, Inc
Journal:
Journal of the American Society for Information Science
Issue Date:
Jun-1998
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/106135
Submitted date:
2004-10-08
Abstract:
Information retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to â â intelligentâ â information retrieval and indexing. More recently, information science researchers have turned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to familiarize readers with the techniques, we present three promising methods: The symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval. An experiment using a 8000-record COMPEN database was performed to examine the performances of these inductive query-by-example techniques in comparison with the performance of the conventional relevance feedback method. The machine learning techniques were shown to be able to help identify new documents which are similar to documents initially suggested by users, and documents which contain similar concepts to each other. Genetic algorithms, in particular, were found to out-perform relevance feedback in both document recall and precision. We believe these inductive machine learning techniques hold promise for the ability to analyze usersâ preferred documents (or records), identify usersâ underlying information needs, and also suggest alternatives for search for database management systems and Internet applications.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Information Science; Artificial Intelligence; Information Extraction
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab

Full metadata record

DC FieldValue Language
dc.contributor.authorChen, Hsinchunen_US
dc.contributor.authorShankaranarayanan, Ganesanen_US
dc.contributor.authorShe, Linlinen_US
dc.contributor.authorIyer, Ananden_US
dc.date.accessioned2004-10-08T00:00:01Z-
dc.date.available2010-06-18T23:41:24Z-
dc.date.issued1998-06en_US
dc.date.submitted2004-10-08en_US
dc.identifier.citationA Machine Learning Approach to Inductive Query by Examples: An Experiment Using Relevance Feedback, ID3, Genetic Algorithms, and Simulated Annealing 1998-06, 49(8):693-705 Journal of the American Society for Information Scienceen_US
dc.identifier.urihttp://hdl.handle.net/10150/106135-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractInformation retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to â â intelligentâ â information retrieval and indexing. More recently, information science researchers have turned to other newer inductive learning techniques including symbolic learning, genetic algorithms, and simulated annealing. These newer techniques, which are grounded in diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information systems. In this article, we first provide an overview of these newer techniques and their use in information retrieval research. In order to familiarize readers with the techniques, we present three promising methods: The symbolic ID3 algorithm, evolution-based genetic algorithms, and simulated annealing. We discuss their knowledge representations and algorithms in the unique context of information retrieval. An experiment using a 8000-record COMPEN database was performed to examine the performances of these inductive query-by-example techniques in comparison with the performance of the conventional relevance feedback method. The machine learning techniques were shown to be able to help identify new documents which are similar to documents initially suggested by users, and documents which contain similar concepts to each other. Genetic algorithms, in particular, were found to out-perform relevance feedback in both document recall and precision. We believe these inductive machine learning techniques hold promise for the ability to analyze usersâ preferred documents (or records), identify usersâ underlying information needs, and also suggest alternatives for search for database management systems and Internet applications.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherWiley Periodicals, Incen_US
dc.subjectInformation Scienceen_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.titleA Machine Learning Approach to Inductive Query by Examples: An Experiment Using Relevance Feedback, ID3, Genetic Algorithms, and Simulated Annealingen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of the American Society for Information Scienceen_US
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