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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
This paper is started from addressing the common automatic method of ontology construction. Then, from viewpoint of the military intelligent processing, the two-level domain ontology architecture is designed. One level is the keyword ontology. The other level is the instance ontology. Different level has different
system that will link the visual and text semantics in order to routinely annotate video sequences with the appropriate keywords of the domain experts' terminology.
Question answering(QA) can vastly improve the quality and effectiveness of knowledge acquisition on the Web. Web search provides a list of documents targeted based on the keywords coined by the users; users need to investigate further the documents to obtain the very knowledge they wanted. QA can come up with the
This paper introduces a new technique of document clustering based on frequent senses. The proposed system, GDClust (graph-based document clustering) works with frequent senses rather than frequent keywords used in traditional text mining techniques. GDClust presents text documents as hierarchical document-graphs and
various explanatory model, all influenced by psychosocial and cultural variations. Semantically annotated documents provide several advantages. One advantage is document specific representations no longer affect the search. This is extremely important in the case of multilingual representations. Keywords of several languages
Current classification methods are based on the ldquobag of wordsrdquo (BOW) representation, which only accounts for term frequency in the documents, and ignores important semantic relationships between key terms. In this paper, we proposed a system that uses ontologies and natural language processing techniques to
Automatic question answering system is a hot issue in the field of natural language processing, and is playing an increasingly important role in the long-distance teaching through networks. This paper proposes an ontology-based automatic question answering system model, at first, build restricted area ontology, then
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.