Abstract
The World Wide Web has become colossal and its growth is also dynamic. Most of the people rely on the search engines to retrieve and share information from various resources. All the results returned by search engines are not always relevant as it is retrieved from heterogeneous data sources. Moreover a naive user finds it difficult to confirm that the retrieved results are significant to the user query. Therefore semantic web plays a major role in interpreting the relevancy of search results. In this work, a novel algorithm is proposed for retrieving relevant documents using semantic web based on the concept of Natural Language processing (NLP). In this proposed system, NLP is used to analyse the user query in terms of Parts of Speech. The extracted terms are compared to the domain dictionary to identify the relevant domain of the user interest. On the other hand, the retrieved documents of the user query are investigated with the help Natural language processing to identify the relevant domain. Now the documents are ranked as per the relevancy of the contents against user query. The experimental result of the proposed algorithm indicates that the accuracy of the retrieved document is 97 %.
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Pandiarajan, S., Yazhmozhi, V.M., Praveen kumar, P. (2015). Semantic Search Engine Using Natural Language Processing. In: Sulaiman, H., Othman, M., Othman, M., Rahim, Y., Pee, N. (eds) Advanced Computer and Communication Engineering Technology. Lecture Notes in Electrical Engineering, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-319-07674-4_53
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DOI: https://doi.org/10.1007/978-3-319-07674-4_53
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