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Traditionally, full text retrieval over structure peer- to-peer network has been implemented by inverted index by keywords. However, search based on this index scheme only support literally word match, not taking into account the meaning of words. In this paper, we present a new index scheme, inverted index by
Various researchers have recognized that ontology based retrieval is one of the best in terms of precision and recall for semantic search engine. However, they did not specify explicitly any scheme/ algorithm for ontology based indexing and retrieval. This motivates us to conceive a proposal that includes (a) suggesting indexing algorithm to effectively maintain ontologies, (b) suggesting retrieving...
Web services like conceptual search, i.e., search based on meaning rather than just character strings, has been the motivation of a large body of research in the IR field.
to the quality of the information. Traditional search engine that uses keyword to search for information about the meaning of a word or a sentence often produce biased and irrelevant search results. Ontology has been developed to overcome this problem. In this paper an ontology framework is presented, which was
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
associated with intermediate semantic descriptors. The intermediate descriptors are used also for image categorization and for qualitative definition of semantic keywords in the user queries. For improving the initial query results, we apply a relevance feedback mechanism that uses the low -level descriptors of the images
Message Oriented Middleware (MOM) is getting popular along with the development of heterogeneous platforms and applications. Most Message Oriented Middleware supports Publish/Subscribe scheme for message interoperation and it usually matches topics directly by matching keywords. Recent Semantic Message Oriented
Conventional document clustering techniques are mainly based on the existence of keywords and the number of occurrences of it. Most of the term frequency based clustering techniques consider the documents as bag-of-words and ignore the important relationships between the words in the document. Phrase based clustering
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
ignores important semantic relationships between key terms. In this paper, we proposed a system that uses ontologies and Natural Language Processing techniques to index texts. Traditional BOW matrix is replaced by "Bag of Concepts" (BOC). For this purpose, we developed fully automated methods for mapping keywords to their
With the increasing popularity of Web 2.0, a general rise of user generated content there are more and more tagging systems that allow users to annotate digital resources with tags (keywords) and share their annotations with other users. Tagging is frequently seen in contrast to traditional knowledge organization
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