Emerging opportunities in Domain Specific Search
Article No.: 16, Pages 1 - 4
Abstract
The cyber space is exploding in rapidity that nobody has ever imagined, it becomes very essential to search the web of cyber space efficiently and effectively. One solution came up as an resolution to this problem is search engines. By now a lot of commercial business search engines have been put on the market. The web has grown up a lot in terms of search engines. However these search engines sometimes respond with awkward and bulky results, this was unbearable for domain specific experts. Numerous domain specific search engines are being developed using a dedicate hardware and a commercial software. These engines are widely used for variety of purposes and topic specific searches. A domain specific search engine is different from a general web search engine; the domain specific search focuses on a specific segment of online content. They are also called specialty or topical search engines. The domain specific content area may be based on different topics i.e. topicality based, type of media, or type of content. General verticals include shopping, the automotives, legal information, medical, literature, and travel. In this paper we will be studying the changing nature of web search in context to domain specific search[1].
References
[1]
B. Geng, L. Yang, C. Xu, and X.-S. Hua, ---Ranking Model Adaptation for Domain-Specific Search,|| IEEE, 2012
[2]
Jiawei Han and Micheline Kamber, ---Data Mining: Concepts and Techniques, Second Edition,|| Issue Date: 2006.
[3]
A. McCallum, K. Nigam, J. Rennie, and K. Seymore, ---A machine learning approach to building domain-specific search engines,|| IJCAI-99, p. 662--667, 1999.
[4]
Michael Chau, ---Spidering and Filtering Web Pages for Vertical Search Engines|| 2002.
[5]
J. Rennie, A. McCallum Using Reinforcement Learning to Spider the Web efficiently.
[6]
Manber, U., Smith, M., and Gopal, B. ---WebGlimpse: Combining Browsing and Searching,|| in Proceedings of the USENIX 1997 Annual Technical Conference, Anaheim, California, Jan 1997.
[7]
Sumner, R. G., Jr., Yang, K., and Dempsey, B. J. ---An Interactive WWW Search Engine for User-defined Collections,|| in Proceedings of the 3rd ACM Conference on Digital Libraries, Pittsburgh, Pennsylvania, USA, Jun 1998, pp. 307--308.
[8]
Witten, I. H., Bainbridge, D., and Boddie, S. J. ---Greenstone: Open-source DL Software,|| Communications of the ACM, 44(5),pp. 47.
[9]
Cho, J., Garcia-Molina, H., and Page, L. ---Efficient Crawling through URL Ordering,|| in Proceedings of the 7th International World Wide Web Conference, Brisbane, Australia, Apr 1998.
[10]
Baujard, O., Baujard, V., Aurel, S., Boyer, C., and Appel, R. D. ---Trends in Medical Information Retrieval on the Internet,|| Computers in Biology and Medicine, 28, 1998, pp. 589--601.
[11]
Koller, D. and Sahami, M. ---Hierarchically Classifying Documents Using Very Few Words,|| in Proceedings of the 14th International Conference on Machine Learning
[12]
Masand, B., Linoff, G., and Waltz, D. ---Classifying News Stories Using Memory Based Reasoning,|| in Proceeedings of the 15th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR'92), pp. 59--64. Iwayama, M. and Tokunaga, T. ---Cluster-based Text
[13]
Categorization: A Comparison of Category Search Strategies,|| in Proceedings of the 18th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR'95), pp. 273--281.
[14]
Wiener, E., Pedersen, J. O., and Weigend, A. S. ---A Neural Network Approach to Topic Spotting,|| in Proceedings of the 4th Annual Symposium on Document Analysis and Information Retrieval (SDAIR'95), 1995.
[15]
Joachims, T. ---Text Categorization with Support Vector Machines: Learning with Many Relevant Features,|| in Proceedings of the European Conference on Machine Learning, Berlin, 1998, pp. 137--142.
[16]
Yang, Y. and Liu, X. ---A Re-examination of Text Categorization Methods, in Proceedings of the 22nd Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR'99), 1999, pp. 42--49.
[17]
Tie-Yan Liu, Microsoft Research Asia, "Learning to Rank for Information Retrieval||, 2008
[18]
S. Kakkar, N. Kumar ---Creating Topic Hierarchy with Clustering in Domain Specific Search Engines|| International Journal of Engineering Science and Advanced Technology, ISSN: 2250-3676 Volume-2, Issue-2, 358--364
[19]
Domingos P (2012) A few useful things to know about machine learning. Commun ACM 55(10)
[20]
Eugene Agichtein, Eric Brill, Susan Dumais, and Robert Ragno. Learning user interaction models for predicting web search result preferences. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '06, pages 3--10, New York, NY, USA, 2006. ACM.
[21]
Krishna Bharat and Monika R. Henzinger. Improved algorithms for topic distillation in a hyperlinked environment. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '98, pages 104--111, 1998.
[22]
Stefan B* uttcher and Charles L. A. Clarke. Index compression is good, especially for random access. In CIKM, pages 761--770, 2007.
[23]
Hong-Wei Hao, Cui-Xia Mu, Xu-Cheng Yin, Shen Li, Zhi-Bin Wang, ---An Improved Topic Relevance Algorithm for focused Crawling||.
[24]
Sk.Abdul Nabi, Dr. P.Premchand, ---Effective Performance of Information Retrieval by using Domain Based Crawler, Vol. 4, No. 7, 2013.
[25]
Gautam Pant, Padmini Srinivasan1, Filippo Menczer, ---Crawling the Web, Dept of Management Sciences, q??=r ?????????=??=f?ï ?I=f?ï ?=????=f?=ROO?OI=r p?K=xOTz q??=p?????=EOMMRF=????=??=g???=????????K=xOUz p?????=???????=??=pbj ml =OMMRI=b????????=OMMRIg??????= o???????=OMMRI=m????=g??????=OMM?=
- Emerging opportunities in Domain Specific Search
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Published In
March 2016
843 pages
ISBN:9781450339629
DOI:10.1145/2905055
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Association for Computing Machinery
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Published: 04 March 2016
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ICTCS '16
ICTCS '16: Second International Conference on Information and Communication Technology for Competitive Strategies
March 4 - 5, 2016
Udaipur, India
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