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suggest the ways that make and renew the ontology, which are related with the keywords that users input in the search engine, automatically for the automatic generation of ontology that is not limited by specific domain. Input keyword and relation keywords become OWL, and the relation among the created OWL is expressed by
semantics, automatically generates a set of formal queries, in the query language of the user's choice, which attempt to capture what the user had in mind when she or he wrote those keywords. The system uses ontologies and a description logics reasoner to perform a semantic enrichment of user keywords to improve the
information using natural language, by means of an ontology-based Question Answering (QA) system [14] and b) complements the specific answers retrieved during the QA process with a ranked list of documents from the Web [3]. Our results show that ontology-based semantic search capabilities can be used to complement and enhance
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
plus noun phrase learning for extraction of activity concepts in Chinese. We also propose an algorithm of relevance measurement for extracting relation instances by binary keywords based on co-occurrence statistics. Finally, we build a practical system of ontology learning through learning relation instances of the
Purely keyword-based text search is not satisfactory because named entities and WordNet words are also important elements to define the content of a document or a query in which they occur. Named entities have ontological features, namely, their aliases, classes, and identifiers. Words in WordNet also have ontological
based on keyword indexing, there are many records in their result lists that are irrelevant to the user's information needs. It is shown that for retrieving more relevant and precise results, the following two points should be concerned: First of all, the query (either it is generated by a human or an intelligent agent
well define these conceptions and the relationship between these conceptions. In this way, errors and failures generated due to misunderstanding the conceptions are reduced, function-based services are much easier to be discovered and combined, and in the meantime, the deficiency in keyword-based search technology of UDDI
General purpose search engines provide users with lists of retrieved documents in response to their queries. The common structure of list elements includes the title of a document, its URL, and small snippet from the text. Snippets are evidence of occurrences of query's keywords in the document. The length of each
In this paper we discuss the fundamental problem of information retrieval on the Web. Information on the Web is not semantically categorized and stored. This research focuses on applying semantic capabilities using ontology on search engine. By using ontology, search engine can search keywords that are conceptually
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