This paper shows how the gap between the texts based web pages and the Resource Descriptive Framework based pages of the semantic web can be bridged by ontologies. Most traditional search engines use indexes that are engineered at the syntactical level and come back hits based mostly on straightforward string comparisons or use the static keyword based indexing. However, the indexes don't contain synonyms, cannot differentiate between homonyms ("mouse" as a Pointing device vs. "Mouse" as a living animal) and users receive completely different search results after they use different conjugation varieties of identical word. During this work, we have a tendency to gift a system that uses ontologies and Natural Language Processing techniques to construct index, and therefore supports word sense disambiguation. Therefore the retrieval of document that contains equivalent term as the context demands is achieved to provide efficient search engines through ontological indexing.
General TermsSemantic indexing theory, Architecture, Algorithm.
Finding meaningful information among the billions of information resources on the Web is a difficult task due to growing popularity of the Internet. The future of World Wide Web (WWW) is the Semantic Web, where ontologies are used to assign (agreed) meaning to the content of the Web. On the Semantic Web, data will inevitably be linked to many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. As the resources on the Semantic Web are annotated using these ontologies, new search techniques are required to find specific information. For this, architecture has been proposed for ontology based semantic web crawler. This architecture can exploit the semantic metadata to efficiently discover and extract information from the Semantic Web. In this paper Semantic matching between content of downloaded web page and ontology is used to guide the crawler towards relevant information.
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