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Integrated Search System using Semantic Analysis


  • School of Computing, SASTRA University, Thanjavur - 613401, Tamil Nadu, India


Objectives: With the rapid growth of technology drastically there is an increase in users of computer system. Search system plays a vibrant role in optimizing the search time. Methods/Statistical Analysis: The integrated search system has the need of improving search accuracy and expanding their coverage of search. This can be done using concepts of semantic analysis. Findings: In this paper an integrated system is formulated by accumulating multiple sources such as local storage, secondary storage and online repositories. The keyword is contextually interpreted by making use of ontologies, using this interpretation of the keyword the required multimedia or/and textual data is searched as intended by the user. Applications: This integrated search system analyzes the meaning of the query and provides the search result according to the intention of the user through is proper expansion of keyword.


Integrated Search, Ontology, Semantic Analysis.

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  • Myung-Eun KIM, Joon-Myun CHO, Jeong-Ju YOO, Jin-Woo HONG, Sang-Ha KIM. A proposal of semantic analysis based multi-level search system for smart TV. ICACT Transactions on Advanced Communications Technology (TACT); 2013 Mar; 2(1):197–205.
  • Bollegala D, Matsuo Y, Ishizuka M. A web search enginebased approach to measure semantic similarity between words. IEEE Transactions on knowledge and data engineering.2011 Jul; 23(7):977–990.
  • Bridging the semantic gap in multimedia information retrieval. Available from;
  • Flickner M, Sawhney H, Niblack W, Ashley J, Don BQ, Gorkani M, Hafner J, Lee D, Petkovic D, Steele D, Yanker P. Query by image and video content: The QBIC system.Journal of Computer. 1995 Sep; 28(9):23–32.
  • Hanjalic A, Lagendijk RL, Biemond J. A new method for key frame based video content representation. Image Databases and Multi-Media Search. 1998; 1–11.
  • Smith JR, Chaang S-F. Visually searching the web for content.Journal of Multi Media. 1997 Jul-Sep; 4(3):12–20.
  • On SVD-free latent semantic indexing for image retrieval for application in a hard industrial environment. Available from:
  • Zhong D, Chang S-F. An integrated approach for contentbased video object segmentation and retrieval. IEEE Transactions on Circuits and Systems for Video Technology.1999 Dec; 9(8):1259–68.
  • Swain MJ, Frankel C, Athitsos V. WebSeer: An image search engine for the world wide web. Challenge of Image Retrieval; Newcastle. 1999. p. 1–8.
  • Barnard K, Duygulu P, Forsyth D, de Freitas N, Blei DM, Jordan MI. Matching words and pictures. Journal of Machine Learning Research. 2003 Jan; 3:1107–35.
  • Camarillo RP, Conde-Ram JC, Sanchez LA. A hybrid approach for solving the semantic annotation problem in semantic social networks. Research in Computing Science.2013; 65:25–33.
  • Social Network Data Retrieving using Semantic Technology.Available from:


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