Abstract
Now, we are flooded with data, yet we are starving for knowledge. The information we get is by using some platforms. Search engine acts as one of such platforms for getting information. So, when a user wants any information from the search engine, it should return some valid information. At first, the user gives his/her query to search engine. The query is matched with the documents present within the database of the search engine. This search engine uses different similarity functions to retrieve correct information from the database of the search engine. This similarity functions uses different mathematical ways to give a score. Now, the document from the database of the search engine which has the most significant score is retrieved. Like this, the results are retrieved and the user gets his/her information.
This is a preview of subscription content, log in via an institution to check access.
References
Raghavan, V.V., Wong, S.M.: A critical analysis of vector space model for information retrieval. J. Am. Soc. information Sci. 37(5), 279 (1986)
Bollegala, D., Matsuo, Y., Ishizuka, M.: Measuring semantic similarity between words using web search engines. www 7, 757–766 (2007)
Gao, R., Li, D., Li, W., Dong, Y.: Application of full text search engine based on lucene. Adv. Internet Things 2(04), 106 (2012)
McCandless, M., Hatcher, E. Gospodnetic, O.: Lucene in action: covers apache Lucene 3.0. Manning Publications Co (2010)
Prez-Iglesias, J., Prez-Agera, J.R., Fresno, V., Feinstein, Y.Z.: Integrating the probabilistic models BM25/BM25F into Lucene (2009). http://arxiv.org/abs/0911.5046
Veer, G., Rathod, P., Sinare, P., Singh, R.B.: Building inverted index and search engine using apache Lucene. Int. J. Adv. Res. Comput Commun. Eng. 4, 426–428 (2015)
Singh, V.K., Singh, V.K.: Vector space model: an information retrieval system. Int. J. Adv. Eng. Res. Studies/IV/II/Jan.–March, 141–143 (2015)
Hchsttter, N., Lewandowski, D.: What users seeStructures in search engine results pages. Inf. Sci. 179(12), 1796–1812 (2009)
Vaughan, L.: New measurements for search engine evaluation proposed and tested. Inf. Process. Manage. 40(4), 677–691 (2004)
Balipa, M., Balasubramani, R.: Search engine using apache lucene. Int. J. Comput. Appl. 127(9), 27–30 (2015)
Dhyani, D., Bhowmick, S.S., Ng, W.K.: Deriving and verifying statistical distribution of a hyperlink-based Web page quality metric. In: Database and Expert Systems Applications, pp. 19–28. Springer, Berlin Heidelberg (2002)
Mukhopadhyay, D., Biswas, P., Kim, Y.C.: A syntactic classification based web page ranking algorithm (2011). http://arxiv.org/abs/1102.0694
Lv, Y., Zhai, C.: Adaptive term frequency normalization for BM25. In Proceedings of the 20th ACM international conference on Information and knowledge management, pp. 1985–1988. ACM (2011)
Hiemstra, D., Robertson, S.E.: Relevance feedback for best match term weighting algorithms in information retrieval, pp. 37–42 (2001)
Haveliwala, T.H.: Topic-sensitive pagerank: a context-sensitive ranking algorithm for web search. IEEE Trans. Knowl. Data Eng. 15(4), 784–796 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Balabantaray, R.C., Ghosh, S. (2018). A Case Study for Ranking of Relevant Search Results. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 564. Springer, Singapore. https://doi.org/10.1007/978-981-10-6875-1_23
Download citation
DOI: https://doi.org/10.1007/978-981-10-6875-1_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6874-4
Online ISBN: 978-981-10-6875-1
eBook Packages: EngineeringEngineering (R0)