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in the emergent ocean of information. The upcoming demand for data storage in petabytes and exabytes of data has also resulted in putting pressure in organizing the file structure in such a way that retrieval results of searching a keyword should match with the growing pace of data storage. As a result, there is an
repository and the associated search techniques viz. keyword-based search and signature matching in a reasoned manner by exemplifying the working methodology of these techniques via its automated implementation. To address the purpose a new tool has been developed name ARE (Automated Repository Exploration) ver. 1.0.0. Readers
A lot of semantic information is lost due to keyword centric approach of information indexing. Web search should be based on `context' of the query and not only on the keywords in query. It is only possible when a context from a query as well as document is sensed and which requires a context based indexing approach
We present a content spotting system for line drawing graphic document images. The proposed system is sufficiently domain independent and takes the keyword based information retrieval for graphic documents, one step forward, to Query By Example (QBE) and focused retrieval. During offline learning mode: we vectorize
generally have problems on keyword-search problem. In this paper, we proposed an initial model to solve the problem by using Case-Based Reasoning (CBR) and Formal Concept Analysis (FCA). For the proposed model, a case base is created to represent design patterns. FCA is used to be case organization that analyze case base for
In an effort to develop effective multi-media learning objects (MLO), we propose a framework to extract and associate semantic tags to temporally segmented instructional videos. These tags serve for the purpose of efficient indexing and retrieval system. We create these semantic tags from potential keywords extracted
relevant matched results to be presented to the user. The quality of the matched result depends on the information stored in the index. The more efficient is the structure of index, more efficient the performance of search engine. Generally, inverted index are based solely on the frequency of keywords present in number of
indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-$k$<alternatives><inline-graphic xlink:href="mouratidis-ieq1-2657622.gif"/> </alternatives> documents that are most relevant to her
and standards of educational metadata. However, this solution does not solve completely the problem. Previously, traditional information retrieval systems rely on indexing by keywords for representing pedagogical resources and queries content. This process, based on lexical matching, allows selecting pedagogical
The information world WWW has more than 3 billion HTML pages and these web pages gain access through search engines only. Search engine is a program that searches the document for specified set of keywords and returns a list of documents where any or all of the specified keywords were found. As more information
of content. The main contribution of FIRSt is an integrated strategy that enables a content-based recommender to infer user interests by applying machine learning techniques, both on official item descriptions provided by a publisher and on freely keywords which users adopt to annotate relevant items. Static content and
Classical IR systems are often based on lexical matching using approaches that rely on purely statistical methods founded on distributions of keywords to calculate the similarity between the query and the documents of the corpus. The relevance of a document according to a query is based on the similarity of vocabulary
provides a novel technique for indexing the web documents based on the context of keywords that helps a search engine to serve a query with more specific documents, and relevant contents.
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
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