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With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we
the interfaces of web services, we make operations defined in WSDL files which compose web services as the base units for searching and organize all information of operations and corresponding components as documents, which will facilitate IR-Style keyword searching. In order to improve the precision of searching, we
From the strong point of XML that allows document owners to describe their documents in their own format, it is difficult to search information if those XML documents use different formats. Moreover, users might not retrieve all relevant information from differently formatted XML documents. To allow users to retrieve all relevant results, users need to have as many as queries for all possible formats...
Keyword based search scheme imposes the problem of representing a lot of web pages in the search engines. Query expansion with relevant words increases the performance of search engines, but finding and using the relevant words is an open problem. In this research we describe a new model for query expansion which
Keyword based information retrieval has difficulties in retrieving relevant information because it is not able to include the semantics of queries. In this paper, we present a novel method for query expansion based on semantic relations. In our proposed algorithm, semantically related words to the query are extracted
Current many information retrieval methods are based on purely keywords for representing the user needs. One of the main problems with this method is that it does not formally capture the explicit meaning of a keywords query, and ignores the documents that may be different in content but related with them. To improve
, first, we consider each individual ontology and user query keywords to determine the Basic Expansion Terms (BET) using a number of semantic measures namely Density Measure (DM), Betweenness Measure (BM), and Semantic Similarity Measure (SSM). Second, we specify New Expansion Terms (NET) by Ontology Alignment (OA). Third
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