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and completeness through sense disambiguation and contextual meta-data prepossessing. Our schemes exploits a linguistic ontology identifying query relevant homographs used to construct sense specific keyword sets allowing for enhanced image search and result ranking via the calculation of relatedness between query
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
Social bookmarking tools are rapidly emerging on the Web as it can be witnessed by the overwhelming number of participants. In such spaces, users annotate resources by means of any keyword or tag that they find relevant, giving raise to lightweight conceptual structures aka folksonomies. In this respect, needless to
presented. Extensible experiment results demonstrate that annotated web services by our proposed method can more satisfy requirements of service requesters than keyword-based described web services. It can achieve higher service discovery effectiveness.
Most of the current focused crawling approaches perform syntactic matching, that is, they retrieve documents that contain particular keywords from the user's query. This often leads to poor discovery results, because the keywords in the query can be semantically similar but syntactically different, or vice-versa
General purpose search engines provide users with lists of retrieved documents in response to their queries. The common structure of list elements includes the title of a document, its URL, and small snippet from the text. Snippets are evidence of occurrences of query's keywords in the document. The length of each
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
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