Intelligent techniques for effective Information Retrieval
Intelligent techniques for effective Information Retrieval A Conceptual Graph based Approach
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This monograph presents a thesis work that attempts to utilize semantics to improve effectiveness in IR. The hypothesis is that with an improved representation of documents and by incorporating limited semantic knowledge, it is possible to improve the effectiveness of an information retrieval (IR) system. In this direction, statistical, hybrid and integrated IR models have been investigated. The conceptual graph (CG) based relevance feedback strategies and CG similarity measures proposed in this thesis are novel contributions. The techniques investigated in this work make use of CG-based model...
This monograph presents a thesis work that attempts to utilize semantics to improve effectiveness in IR. The hypothesis is that with an improved representation of documents and by incorporating limited semantic knowledge, it is possible to improve the effectiveness of an information retrieval (IR) system. In this direction, statistical, hybrid and integrated IR models have been investigated. The conceptual graph (CG) based relevance feedback strategies and CG similarity measures proposed in this thesis are novel contributions. The techniques investigated in this work make use of CG-based model in conjunction with the statistical vector space model. The CG-based model attempts to capture relationship among terms (concept). The two models thus complement each other allowing us to take the benefits of the long and established research efforts in the statistical models and versatility of semantic models. The chapters in this monograph have enough material for young researchers working in this area and may give rise to interesting research problem for their work.
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