Persistent Link:
http://hdl.handle.net/10150/105191
Title:
Inductive Query by Examples (IQBE): A Machine Learning Approach
Author:
Chen, Hsinchun; She, Linlin
Citation:
Inductive Query by Examples (IQBE): A Machine Learning Approach 1994,
Issue Date:
1994
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105191
Submitted date:
2004-10-01
Abstract:
This paper presents an incremental, inductive learning approach to query-by examples for information retrieval (IR) and database management systems (DBMS). After briefly reviewing conventional information retrieval techniques and the prevailing database query paradigms, we introduce the ID5R algorithm, previously developed by Utgoff, for ``intelligent'' and system-supported query processing. We describe in detail how we adapted the ID5R algorithm for IR/DBMS applications and we present two examples, one for IR applications and the other for DBMS applications, to demonstrate the feasibility of the approach. Using a larger test collection of about 1000 document records from the COMPEN CD-ROM computing literature database and using recall as a performance measure, our experiment showed that the incremental ID5R performed significantly better than a batch inductive learning algorithm (called ID3) which we developed earlier. Both algorithms, however, were robust and efficient in helping users develop abstract queries from examples. We believe this research has shed light on the feasibility and the novel characteristics of a new query paradigm, namely, inductive query-by examples (IQBE). Directions of our current research are summarized at the end of the paper.
Type:
Conference Paper
Language:
en
Keywords:
Databases; 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.contributor.authorShe, Linlinen_US
dc.date.accessioned2004-10-01T00:00:01Z-
dc.date.available2010-06-18T23:21:21Z-
dc.date.issued1994en_US
dc.date.submitted2004-10-01en_US
dc.identifier.citationInductive Query by Examples (IQBE): A Machine Learning Approach 1994,en_US
dc.identifier.urihttp://hdl.handle.net/10150/105191-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractThis paper presents an incremental, inductive learning approach to query-by examples for information retrieval (IR) and database management systems (DBMS). After briefly reviewing conventional information retrieval techniques and the prevailing database query paradigms, we introduce the ID5R algorithm, previously developed by Utgoff, for ``intelligent'' and system-supported query processing. We describe in detail how we adapted the ID5R algorithm for IR/DBMS applications and we present two examples, one for IR applications and the other for DBMS applications, to demonstrate the feasibility of the approach. Using a larger test collection of about 1000 document records from the COMPEN CD-ROM computing literature database and using recall as a performance measure, our experiment showed that the incremental ID5R performed significantly better than a batch inductive learning algorithm (called ID3) which we developed earlier. Both algorithms, however, were robust and efficient in helping users develop abstract queries from examples. We believe this research has shed light on the feasibility and the novel characteristics of a new query paradigm, namely, inductive query-by examples (IQBE). Directions of our current research are summarized at the end of the paper.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectDatabasesen_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.titleInductive Query by Examples (IQBE): A Machine Learning Approachen_US
dc.typeConference Paperen_US
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