Applying genetic algorithms to query optimization in document retrieval

This paper proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. One of the contributions of the paper is to combine the Bigram (Chen, A., He, J., Xu, L., Gey, F. C., & Meggs, J. 1997. Chinese text retrieval without using a di...

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Bibliographic Details
Published in:Information processing & management Vol. 36; no. 5; pp. 737 - 759
Main Authors: Horng, Jorng-Tzong, Yeh, Ching-Chang
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.09.2000
Elsevier Science
Elsevier Science Ltd
Subjects:
Algorithms
Bigram Strategy
Chinese
Content analysis
Exact sciences and technology
Genetic algorithms
Genetics
Indexing. Classification. Abstracting. Syntheses
Information and communication sciences
Information and document structure and analysis
Information processing and retrieval
Information Retrieval
Information science. Documentation
Keyword
Keywords
Mathematical Formulas
Online information retrieval
Optimization
PAT-tree
Query Processing
Relevance (Information Retrieval)
Retrieval
Sciences and techniques of general use
Searching
Studies
Vector Spaces
Weighted Term Searching
Weighting
Keyword
Information retrieval
PAT-tree
Performance evaluation
Automatic classification
Weighting
Genetic algorithm
Automatic indexing
Feedback regulation
Cluster
Method
Comparative study
Optimization
ISSN:0306-4573, 1873-5371
Online Access:Get full text
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Summary:This paper proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. One of the contributions of the paper is to combine the Bigram (Chen, A., He, J., Xu, L., Gey, F. C., & Meggs, J. 1997. Chinese text retrieval without using a dictionary, ACM SIGIR’97, Philadelphia, PA, USA, pp. 42–49; Yang, Y.-Y., Chang, J.-S., & Chen, K.-J. 1993), Document automatic classification and ranking, Master thesis, Department of Computer Science, National Tsing Hua University) model and PAT-tree structure (Chien, L.-F., Huang, T.-I., & Chien, M.-C. 1997 Pat-tree-based keyword extraction for Chinese information retrieval, ACM SIGIR’97, Philadelphia, PA, US, pp. 50–59) to retrieve keywords. The approach extracts bigrams from documents and uses the bigrams to construct a PAT-tree to retrieve keywords. The proposed approach can retrieve any type of keywords such as technical keywords and a person’s name. Effectiveness of the proposed approach is demonstrated by comparing how effective are the keywords found by both this approach and the PAT-tree based approach. This comparison reveals that our keyword retrieval approach is as accurate as the PAT-tree based approach, yet our approach is faster and uses less memory. The study then applies genetic algorithms to tune the weight of retrieved keywords. Moreover, several documents obtained from web sites are tested and experimental results are compared with those of other approaches, indicating that the proposed approach is highly promising for applications.
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ISSN:0306-4573
1873-5371
DOI:10.1016/S0306-4573(00)00008-X