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With the development of Internet, more and more on-line information has become precious wealth that we can access to. High quality information is often stored in dedicated digital libraries. However, query system of most digital libraries based on keyword matching couldnpsilat make users satisfied. This paper presents
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
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
competitors, track the developed trend of the industrial technique and improve the independent innovation capability and core competitiveness of their own. But at present patent information retrieval systems based on the keyword match can't satisfy the users' demands on accuracy and completeness. So, this paper proposes to
Inherent ambiguity of short keyword queries demands for enhanced methods for Web retrieval. In this paper we propose to have twice query expansion, the first query expansion is progressed by determining the relatedness between two word senses via structural and domain relatedness computation based on WordNet and
The ineffectiveness of information retrieval systems is mostly caused by the inaccurate query formed by a few keywords that reflect actual user information need. One well known technique to overcome this limitation is Automatic Query Expansion (AQE), whereby the user's original query is improved by adding new features
keywords to determine the Basic Expansion Terms (BET) using a number of semantic measures including Betweenness Measure (BM) and Semantic Similarity Measure (SSM). We propose a Map/Reduce distributed algorithm for calculating all the shortest paths in ontology graph. Map/Reduce algorithm will improve considerably the
Recently, Folksonomy attracts attentions as a new method to index large-scale image databases. In the Folksonomy-style image databases, they allows users to attach keywords to images as “tags”. Since tag words are uncontrolled, they have various and many kinds of tags associated with images. This is much
, 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
based on ontology. It uses the rich semantic knowledge of ontology to upgrade the retrieval based on keywords to concepts, and combines it with the specialized engine to improve retrieval effect and efficiency. The paper also takes patent information for example to explain its application at the end.
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