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This paper investigates the semantic search performance of search engines. Initially, three keyword-based search engines (Google, Yahoo and Msn) and a semantic search engine (Hakia) were selected. Then, ten queries, from various topics, and four phrases, having different syntax but similar meanings, were determined
Metacat to improve metadata search in multiple ways: (i) by expanding standard keyword searches with ontology term hierarchies; (ii) by allowing keyword searches to be applied to annotations in addition to traditional meta-data; and (iii) by allowing more structured searches over annotations via ontology terms. We describe
Under grid environment, a resource discovery mechanism could dramatically affect both performance and efficiency of the system. Currently, however, since most models of grid discovery are centralized, keyword based matching ones, outcomes of discovery could seldom cater customers' needs. This paper puts forward a grid
This paper describes a new approach of enhancing textual document search and retrieval. The approach tries to take advantage of structured query languages in search and retrieval. For this purpose the semantic model of the document is created. The semantic model of the document is an ontology-like structured semantic annotation of the document that can support structured querying. This paper discusses...
Exploring the metadata associated with documents in the semantic Web is a way to increase the precision of information retrieval systems. Systems have been established so far failed to overcome fully the limitations of search based on keywords. Such systems are built from variations of classic models that represent
On account of the weakness of the current search engine, we put forward the concept of semantic search. Integrated with the unstructured information management architecture (UIMA) of IBM, we can make search not only just base on the search of keywords, but through the comprehension of the unstructured information to
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