Artificial Intelligence Lab, Department of MIS, University of Arizona / 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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105191 |
| Date | January 1994 |
| Creators | Chen, Hsinchun, She, Linlin |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Conference Paper |
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