Semantic Desktop Search Engine using Graph Database
Soumya George1, M. Sudheep Elayidom2, T. Santhanakrishnan3
1Soumya George, Research Scholar, Department of Computer Applications, Cochin University of Science and Technology, Kochi (Kerala), India.
2M. Sudheep Elayidom, Associate Professor, Division, Department of Computer Engineering, Cochin University of Science and Technology Kochi (Kerala), India.
3T. Santhanakrishnan, Scientist, Govt. of India, Ministry of Defense, Naval Physical and Oceanographic Laboratory Thrikkakkara, Kochi (Kerala), India.
Manuscript received on 22 May 2019 | Revised Manuscript received on 08 June 2019 | Manuscript Published on 15 June 2019 | PP: 373-375 | Volume-8 Issue-1S2 May 2019 | Retrieval Number: A00870581S219/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The rise of big data with advancement in technology leads to an ever-increasing demand for a personalized search engine to search the huge amount of data residing in personal computers. A desktop search engine is used to search files or data in a user’s personal systems. This paper proposes a graph based semantic desktop search engine, GSDSE that uses the Word Sequence Graph model to store the file details and contents inside a graph database using full text indexing approach. The main features of GSDSE include content-based query autosuggestion based on entire query term sequence, link based page ranking, the semantic search of different query combinations and generation of content based valid search snippet view. To prove the efficiency and reliability of GSDSE, we conduct a comparsion study between Copernic Desktop search engine and GSDSE, and the results proved that the proposed system is efficient concerning efficiency and reliability.
Keywords: Desktop Search Engine, Graph Database, Word Sequence Graph Model, Semantic Search Engine.
Scope of the Article: Database Theory and Application