Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:3 Issue:2 Year: 14 April,2014 Pages:375-383
Mobile device communication with users serves various purposes such as location search, map, navigation etc. It also helps user connect with search engines. But the search query is limited to small words unlike those used when interacting with search engines through computers. This leads to drawback in effective communication between the user and the server through mobile device, as there are limitations in mobile device. Hence our proposed solution aids in better and faster result retrieval from querying search engine through mobile by using user’s profile information in a secure way. Ontology ranked keyword search algorithm is used to examine and clean search queries and rank results accordingly. Users search history is stored locally and search results are provided by the server in preference to existing search history information. The search history preferences are categorize based on mining the content and location information along with the user’s profile. Ranking of results helps the end user in easy access to the needed source, thus proving to be efficient. Our proposed system provides an innovative approach of searching the data on the input text, pattern of the text, spatial information relative search, User type specific search and finally Ontology based Search.
Ontology, user preference, opinion mining, ranking function
[1] Agichtein E, Brill E, and Dumais S,2006: 'Improving Web Search Ranking by Incorporating User Behavior Information', Proc. 29th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR). [2] Chen Y.-Y, Suel T, and Markowetz A, 2006: ‘Efficient Query Processing in Geographic Web Search Engines’, Proc. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR). [3] Gan Q, Attenberg J, Markowetz A, and Suel T., 2008: ‘Analysis of Geographic Queries in a Search Engine Log’, Proc. First Int’l Workshop Location and the Web (LocWeb). [4] Joachims T, 2002: ‘Optimizing Search Engines Using Click through Data’, Proc. ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining. [5] Kenneth Wai-Ting Leung, Dik Lun Lee, and Wang-Chien Lee., april 2013: ‘PMSE: A Personalized Mobile Search Engine’, ieee transactions on knowledge and data engineering, vol. 25, no. 4. [6] Liu B, Lee W.S, Yu P.S, and Li X., 2002: ‘Partially Supervised Classification of Text Documents’, Proc. Int’l Conf. Machine Learning (ICML). [7] Leung K.W.-T, Lee D.L, and Lee W.-C., 2010: ‘Personalized Web Search with Location Preferences’, Proc. IEEE Int’l Conf. Data Mining (ICDE). [8] Leung K.W.-T, Ng W, and Lee D.L., Nov. 2008: ‘Personalized Concept-Based Clustering of Search Engine Queries’, IEEE Trans. Knowledge and Data Eng., vol. 20, no. 11, pp. 1505-1518. [9] Ng W, Deng L, and Lee D.L., 2007: ‘Mining User Preference Using Spy Voting for Search Engine Personalization’, ACM Trans. Internet Technology, vol. 7, no. 4. [10] Pong J.Y.-H, Kwok R.C.-W, Lau R.Y.-K, Hao J.-X, and Wong P.C.-C, 2008: 'A Comparative Study of Two Automatic Document Classification Methods in a Library Setting', J. Information Science, vol. 34, no. 2, pp. 213-230. [11] Shannon C.E, 1951: 'Prediction and Entropy of Printed English', Bell Systems Technical J., vol. 30, pp. 50-64, 1951. [12] Yokoji S, 2001: 'Kokono Search: A Location Based Search Engine', Proc. Int’l Conf. World Wide Web (WWW).