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    Inductive Query by Examples (IQBE): A Machine Learning Approach

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    Author
    Chen, Hsinchun
    She, Linlin
    Issue Date
    1994
    Submitted date
    2004-10-01
    Keywords
    Databases
    Information Extraction
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial intelligence lab
    AI lab
    Information retrieval
    
    Metadata
    Show full item record
    Citation
    Inductive Query by Examples (IQBE): A Machine Learning Approach 1994,
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105191
    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
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