Probabilistic retrieval revisited
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A semantic Web-based search method is introduced that automates the correlation of topic-related content for discovery of hitherto unknown intelligence from disparate and widely diverse Web-sources. This method is in contrast to traditional search methods that are constrained to specific or narrowly defined topics. The method is based on algorithms from Natural Language Processing combined with techniques adapted from grounded theory and Dempster-Shafer theory to significantly enhance the discovery of related Web-sourced intelligence. This paper describes the development of the method by showing the integration of the mathematical models used. Real-world worked examples demonstrate the effectiveness of the method with supporting performance analysis, showing that the quality of the extracted content is significantly enhanced comparing to the traditional Web-search approaches.