Abstract
Web crawling and word sensing are critical nowadays. In case of web browsing, searching consume time in case proposer requirements from user is not extracted. In earlier work on web browsers word correction was missing which is a main inclusion in the proposed work. The problem with existing literature is time complexity in fetching the correct keyword from user query string. We propose character shuffle pre-processing searching mechanism. Using the proposed method, time complexity is reduced since clustering is used for searching the keywords. The searching don’t required entire database to be searched over rather only particular cluster is searched. To fetch meaningful keywords database is maintained. The keywords within the database increases as more and more user interact with this search engine. The worth of this study is proved using parameters execution time and number of meaningful keywords.
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References
Che, W., Liu, T.: Using word sense disambiguation for semantic role labeling. In: 2010 4th International Universal Communication Symposium, pp. 167–174 (2010)
Features, S.: A method for word sense disambiguation combining context semantic features, pp. 283–287 (2016)
Zeng, Y., et al.: User-centric query refinement and processing using granularity-based strategies. Knowl. Inf. Syst. 27(3), 419–450 (2011)
De Maio, C., Fenza, G., Loia, V., Orciuoli, F.: Unfolding social content evolution along time and semantics. Future Gener. Comput. Syst. 66, 146–159 (2017)
Shekarpour, S., Marx, E.: RQUERY : rewriting natural language queries on knowledge graphs to alleviate the vocabulary mismatch problem (2017)
Mohey, D., Hussein, E.M.: A survey on sentiment analysis challenges. J. King Saud Univ. - Eng. Sci. 30, 330–338 (2016)
Mahajan, S., Sharma, S., Rana, V.: Design a perception based semantics model for knowledge extraction. Int. J. Comput. Intell. Res. 13(6), 1547–1556 (2017)
Chandra, S.: A brief study and analysis of different searching algorithms (2017)
Bollegala, D., Matsuo, Y., Ishizuka, M.: A web search engine-based approach to measure semantic similarity between words. IEEE Trans. Knowl. Data Eng. 23(7), 977–990 (2011)
Navigli, R., Crisafulli, G.: Inducing word senses to improve web search result clustering. Computational Linguistics, pp. 116–126 (2010)
Rana, V.: An approaches & comprehensive survey for measuring semantic relatedness with knowledge resources, vol. 6, no. 1, pp. 398–403 (2018)
Wang, Y.: K-nearest neighbor clustering algorithm based on kernel methods, pp. 0–3 (2010)
Haider, A., Raza, A.: Keyword and form based semantic search tools and their usability. In: 8th International Conference on Digital Information Management ICDIM 2013, pp. 85–89 (2013)
Ashkan, A., Clarke, C.L.A.: Impact of query intent and search context on clickthrough behavior in sponsored search. Knowl. Inf. Syst. 34(2), 425–452 (2013)
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Sunita, Rana, V. (2019). Optimizing Performance of User Web Browsing Search. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 956. Springer, Singapore. https://doi.org/10.1007/978-981-13-3143-5_20
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DOI: https://doi.org/10.1007/978-981-13-3143-5_20
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