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Information Retrieval in the Context of Checking Semantic Similarity in Web: Vision of Future Web
Objective: This work discusses about web search based on content present in the web page. Web 3.0 in this field has been a major invention done till this date. Yet semantic web proposes to be a good choice for its enhancement towards searches based on intent of user. There has been lots of techniques proposed so far still methods are been searched based on this concept. Methods: Surfing the internet for relevant information as needed by the user has therefore become an issue for users. In order to overcome the problem of information deluge for relevant information retrieval, there is a need to devise a way to search data based on user’s intent. This is where semantic search comes into play. Findings: In order to improve quality of search correctness, semantic search tries to interpret the context of data provided along with user’s intent, location from where that has been looked for, variation of words etc. As a part of our work we have proposed a model using natural language processing that aims to classify data based on semantic relevance to user query. The well-known concept of NLP permits the machines to derive the purpose or the meaning meant by humans or natural language input. The aim is to provide exact needed result to the user in place of making him to look for it all over the URLs given as result. Improvements/Applications: Tools based on this semantic knowledge will increase relevance of search engines along with the less list of documents o go through by the user or surfer for a specific result.
Information Retrieval (IR), Natural Language Processing (NLP), Semantic Web, Semantic Relevance, Stop Words
- Sahu SK, Saurabh JN. Comparative analysis of semantic and syntactic based search engines. Journal of Advanced Computing and Communication Technologies. 2016 Apr; 4(2):1–6.
- Babekr ST, Fouad KM, Arshad N. Personalized semantic retrieval and summarization of web based documents. (IJACSA) International Journal of Advanced Computer Science and Applications. 2013; 4(1):177–86.
- Shah U, Finin T, Joshi AR. Cost S, Mayfield J. Information retrieval on the semantic web. CIKM ‘02 Proceedings of the Eleventh International Conference on Information and Knowledge Management. 2002. p. 461–8.
- Harb HM, Fouad KM. Semantic retrieval approach for web documents. (IJACSA) International Journal of Advanced Computer Science and Applications. 2011 Sep; 2(9):1–10.
- Tomassen SL. Research on ontology-driven information retrieval. On the Move to Meaningful Internet Systems 2006: OTM 2006 Workshops. 2006 Nov; 1460–68.
- Sakthi Murugan RP, Aghila SBG. Ontology based information retrieval - An analysis. International Journal of Advanced Research in Computer Science and Software Engineering. 2013 Oct; 3(1):110–20.
- Page L, Brin S, Motwani R, Winograd T. The page Rank citation ranking: bringing order to the web. Technical Report, Stanford Info Lab. 1998 Jan; 1–17.
- Brin S, Page L. The Anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems. 1998 Apr; 30(1-7):107–17.
- Xing W, Ghorbani A. Weighted Page Rank Algorithm. 2004 Proceedings Second Annual Conference on Communication Networks and Services Research. 2004 May. p. 305–14.
- Ju S, Wang Z, Lv X. Improvement of page ranking algorithm based on timestamp and link. International Symposiums on Information Processing, Moscow. 2008 May; 36–40.
- Sadhwani AA, Saxena N. A New Approach to Ranking AlgorithmCustom Personalized Searching. 2nd International Conference on Computing for Sustainable Global Development (INDIACom), Delhi. 2015. p. 130–33.
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