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This system proposes Indian-logic ontology based Context-aware Query Refinement model to support context-sensitive semantic search in keyword based search engine. This is by formulating effective query using Indian logic based Ontology for Context identification to overcome ambiguous query terms and increase the
The number of available Web services, nowadays, is growing rapidly due their potential in many fields. As a result, the discovery process becomes a challenging issue. Traditional syntactic keywords based discovery techniques are inefficient as they fail to recognize similarities between Web services capabilities. Thus
Traditionally information retrieval consists mainly of determining which documents of a collection contain the keywords in the user query. However, a growing number of tasks, especially those related to Semantic Web technologies and applications rely on accurately measuring the similarity between documents and online
index texts. Traditional BOW matrix is replaced by ldquoBag of Conceptsrdquo (BOC). For this purpose, we developed fully automated methods for mapping keywords to their corresponding ontology concepts. Support vector machine a successful machine learning technique is used for classification. Experimental results shows that
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.