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We present in this paper, a context-driven search engine called XCDSearch for answering XML Keyword-based queries as well as Loosely Structured queries, using a stack-based sort-merge algorithm. Most current research is focused on building relationships between data elements based solely on their labels and proximity
form of an ontology which represents the distinct areas of Software Engineering knowledge inspired by SWEBOK (Software Engineering Body of Knowledge). Finally, the process of the classification of texts within the ontology is carried out in three steps: keyword analysis, processing of the document. We believe our proposal
Q( “t1”,…, “tn”, R1?,…, Rn?), where ti denotes a GO search term and Ri denotes a return/result GO term. The result of the Keyword-based query Q(“t1”, “t2”) is a
Concept-based information retrieval offers a higher abstraction level than the classical keyword-based approach. A multilingual terminology makes possible language-independent indexing and querying. The conceptual structure with its associated terminology is called an ontology. This paper describes a methodology to
Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based
, based on a semantic service-oriented approach. KnowleTracker has powerful deep mining functions to pull out news and other information that may lie several layers below the front page based on a semantic search for not only the specific keyword, but also the associated concepts that are not part of the keywords. The
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