• Debajyoti MukhopadhyayEmail author
  • Sukanta Sinha
  • Sukanta Sinha
Part of the Cognitive Intelligence and Robotics book series (CIR)


An overview of Web search engine and domain-specific Web search engine is presented in this chapter. Some recent issues in these areas and the methodology employed are also discussed.


  1. 1.
    T. Berners-Lee, M. Fischetti, Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor (HarperBusiness, New York, 1999)Google Scholar
  2. 2.
    B.M. Leiner, V.G. Cerf, D.D. Clark, R.E. Kahn, L. Kleinrock, D.C. Lynch, J. Postel, L.G. Roberts, S. Wolff, A brief history of internet. ACM Comput. Commun. 35(1), 22–31 (2009). Scholar
  3. 3.
    W. Willinger, R. Govindan, S. Jamin, V. Paxson, S. Shenker, Scaling phenomena in the internet, in Proceedings of the National Academy of Sciences (New York, 2002), pp. 2573–2580Google Scholar
  4. 4.
    J.J. Rehmeyer, Mapping a medusa: the internet spreads its tentacles. Sci. News 171(25), 387–388 (2007). Scholar
  5. 5.
    M.E. Bates, D. Anderson, Free, fee-based and value-added information services Factiva, in The Factiva 2002 White Paper Series (Dow-Jones Reuters Business Interactive, LLC, 2002)Google Scholar
  6. 6.
    D. Hawking, N. Craswell, P. Bailey, K. Griffihs, Measuring search engine quality. Inf. Retrieval 4(1), 33–59 (2001) (Elsevier)CrossRefGoogle Scholar
  7. 7.
    T. Joachims, Optimizing search engines using clickthrough data, in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD02 (Edmonton, Alberta, Canada, 2002), pp. 133–142Google Scholar
  8. 8.
    D. Mukhopadhyay, S.R. Singh, in Two Novel Methodologies for Searching the Web: Confidence Based and Hyperlink-Content Based. Haldia Institute of Technology, Department of Computer Science & Engineering Research Report (2003)Google Scholar
  9. 9.
    R. Baeza-Yates, C. Hurtado, M. Mendoza, G. Dupret, Modeling user search behavior, in Proceedings of the Third Latin American Web Congress, LA-WEB2005 (Buenos Aires, Argentina, 2005), pp. 242–251Google Scholar
  10. 10.
    O. Hoeber, Web information retrieval support systems: the future of Web search, in IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT’08 (IEEE Computer Society, 2008), pp. 29–32Google Scholar
  11. 11.
    T.P.C. Silva, E.S. de Moura, J.M.B. Cavalcanti, A.S. da Silva, M.G. de Carvalho, M.A. Gonc-alves, An evolutionary approach for combining different sources of evidence in search engines. Inf. Syst. 34, 276–289 (2009) (Elsevier)CrossRefGoogle Scholar
  12. 12.
    J.L. Hong, E.G. Siew, S. Egerton, Information extraction for search engines using fast heuristic techniques. Data Knowl. Eng. 69, 169–196 (2010) (Elsevier)CrossRefGoogle Scholar
  13. 13.
    M. Zimmer, Web search studies: multidisciplinary perspectives on Web search engines, in International Handbook of Internet Research (Springer, 2010), pp. 507–521Google Scholar
  14. 14.
    R. Ozcan, I.S. Altingovde, Ö. Ulusoy, Exploiting navigational queries for result presentation and caching in Web search engines. J. Am. Soc. Inform. Sci. Technol. 62(4), 714–726 (2011)CrossRefGoogle Scholar
  15. 15.
    B.B. Cambazoglu, I.S. Altingovde, R. Ozcan, O. Ulusoy, Cache-based query processing for search engines. ACM Trans. Web 6(4), 24 (2012). (Article 14)CrossRefGoogle Scholar
  16. 16.
    A. Papagelis, C. Zaroliagis, A collaborative decentralized approach to Web search. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 42(5), 1271–1290 (2012)CrossRefGoogle Scholar
  17. 17.
    E. Manica, C.F. Dorneles, R. Galante, Handling temporal information in Web search engines. SIGMOD Rec. 41(3), 15–23 (2012)CrossRefGoogle Scholar
  18. 18.
    D. Fuentes-Lorenzo, N. Fernández, J.A. Fisteus, L. Sánchez, Improving large-scale search engines with semantic annotations. Exp. Syst. Appl. 40, 2287–2296 (2013) (Elsevier)CrossRefGoogle Scholar
  19. 19.
    J.C. Prates, E. Fritzen, S.W.M. Siqueira, M.H.L.B. Braz, L.C.V. de Andrade, Contextual Web searches in Facebook using learning materials and discussion messages. Comput. Hum. Behav. 29, 386–394 (2013) (Elsevier)CrossRefGoogle Scholar
  20. 20.
    J.B. Killoran, How to use search engine optimization techniques to increase website visibility. IEEE Trans. Personal Commun. 56(1), 50–66 (2013)CrossRefGoogle Scholar
  21. 21.
    H. Yan, J. Wang, X. Li, L. Gu, Architectural design and evaluation of an efficient Web-crawling system. J. Syst. Softw. 60(3), 185–193 (2002)CrossRefGoogle Scholar
  22. 22.
    J.Y. Yang, J.B. Kang, J.M. Choi, A focused crawler with document segmentation, in Intelligent Data Engineering and Automated Learning Ideal. Lecture Notes in Computer Science, vol. 3578 (2005), pp. 94–101Google Scholar
  23. 23.
    P. Srinivasan, F. Menczer, G. Pant, A general evaluation framework for topical crawlers. Inf. Retrieval 8(3), 417–447 (2005). (Elsevier)CrossRefGoogle Scholar
  24. 24.
    D. Mukhopadhyay, S. Mukherjee, S. Ghosh, S. Kar, Y. Kim, Architecture of a scalable dynamic parallel webcrawler with high speed downloadable capability for a Web search engine, in The 6th International Workshop MSPT 2006 Proceedings (Youngil Publication, Republic of Korea, 2006), pp. 103–108Google Scholar
  25. 25.
    T.R. Gruber, A translation approach to portable ontologies. Knowl. Acquisit. 5(2), 199–220 (1993)CrossRefGoogle Scholar
  26. 26.
    N.F. Noy, D.L. McGuinness, in Ontology Development 101: A Guide to Creating Your First Ontology (Stanford University, Stanford, CA, 2008). Available on: Accessed 2008
  27. 27.
    D.N. Antonio, M. Michele, N. Roberto, A software engineering approach to ontology building. Inf. Syst. 34(2), 258–275 (2009). (Elsevier)CrossRefGoogle Scholar
  28. 28.
    G.A. Miller, R. Beckwith, C.D. Fellbaum, D. Gross, K. Miller, WordNet: an online lexical database. Int. J. Lexicograph 3(4), 235–244 (1990)CrossRefGoogle Scholar
  29. 29.
    S.M. Harabagiu, G.A. Miller, D.I. Moldovan, WordNet 2—a morphologically and semantically enhanced resource, in The Proceeding of the ACL SIGLEX Workshop: Standardizing Lexical Resources (1999), pp. 1–8Google Scholar
  30. 30.
    A. Gangemi, R. Navigli, P. Velardi, The OntoWordNet project: extension and axiomatization of conceptual relations in WordNet, in Proceedings of International Conference on Ontologies, Databases and Applications of Semantics, ODBASE 2003 (Catania, Sicily, Italy, 2003), pp. 820–838Google Scholar
  31. 31.
    D. Mukhopadhyay, A. Kundu, R. Dutta, Y. C. Kim, An idea to minimize memory requirement and redundancy adopting cellular automata while building index file by Web search engine, in 6th International Workshop MSPT 2006 Proceedings (Youngil Publication, Republic of Korea, 2006), pp. 67–79Google Scholar
  32. 32.
    D. Mukhopadhyay, R. Dutta, A. Kundu, Y. C. Kim, A model for Web page prediction using cellular automata, in 6th International Workshop MSPT 2006 Proceedings (Youngil Publication, Republic of Korea, 2006), pp. 95–100Google Scholar
  33. 33.
    R. Dutta, A. Kundu, D. Mukhopadhyay, Y.C. Kim, An alternate approach for efficient Web page prediction, in International Conference on Electronics & Information Technology Convergence, EITC 2006 Proceedings (Yang Dong Publication, Republic of Korea, 2006), pp. 197–203Google Scholar
  34. 34.
    R. Dutta, A. Kundu, D. Mukhopadhyay, Clustering based Web page prediction. Int. J. Knowl. Web Intell. 2(4), 257–271 (2011) (Inderscience Publishers, UK)CrossRefGoogle Scholar
  35. 35.
    L. Page, S. Brin, R. Motwani, T. Winograd, in The PageRank citation ranking: bringing order to the web. Technical report (SIDL-WP-1999-0120) (Stanford InfoLab, 1999)Google Scholar
  36. 36.
    J. Cho, S. Roy, R.E. Adams, Page quality: in search of an unbiased Web ranking, in The Proceedings of ACM SIGMOD International Conference on Management of Data, SIGMOD05 (Baltimore, Maryland, 2005), pp. 551–562Google Scholar
  37. 37.
    D. Mukhopadhyay, D. Giri, S.R. Singh, An approach to confidence based page ranking for user oriented Web search. ACM SIGMOD Rec. 32(2), 28–33 (2003) (ACM Press, New York, USA)CrossRefGoogle Scholar
  38. 38.
    D. Mukhopadhyay, P. Biswas, Y. C. Kim, A syntactic classification based Web page ranking algorithm, in 6th International Workshop MSPT 2006 Proceedings (Youngil Publication, Republic of Korea, 2006), pp. 83-92Google Scholar
  39. 39.
    M. Richardson, A. Prakash, E. Brill, Beyond PageRank: machine learning for static ranking, in 15th International Conference on World Wide Web, WWW06 (Edinburgh, Scotland, 2006), pp. 707–715Google Scholar
  40. 40.
    S. Bao, G. Xue, X Wu, Y. Yu, B. Fei, Z. Su, Optimizing Web search using social annotations, in 16th International Conference on World Wide Web, WWW07 (Banff, Alberta, Canada, 2007), pp. 501–510Google Scholar
  41. 41.
    S. Wadwekar, D. Mukhopadhyay, A ranking algorithm integrating vector space model with semantic metadata, in CUBE 2012 International IT Conference, CUBE 2012 Proceedings (Pune, India, ACM Digital Library, USA, 2012), pp. 623–628Google Scholar
  42. 42.
    A. Bernardini, C. Carpineto, M. D’Amico, Full-subtopic retrieval with keyphrase-based search results clustering, in Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT09 (Italy, 2009), pp. 206–213Google Scholar
  43. 43.
    G. Poonkuzhali, R.K. Kishore, R.K. Kripa, K. Sarukesi, Statistical approach for improving the quality of search engine, in Recent Research in Applied Computer and Applied Computational Science (Venice, Italy, 2011), pp. 89–93Google Scholar
  44. 44.
    B.J. Jansen, P.R. Molina, The effectiveness of Web search engines for retrieving relevant ecommerce links. Inf. Process. Manag. 42, 1075–1098 (2005). (Elsevier)CrossRefGoogle Scholar
  45. 45.
    G. Sudeepthi, G. Anuradha, M.S.P. Babu, A survey on semantic Web search engine. Int. J. Comput. Sci. Issues 9(2), 241–245 (2012)Google Scholar
  46. 46.
    P. Kristen, B. Joanna, R. Lee, Search engine use 2012, in Pew Research Center’s Internet & American Life Project, PewResearchCenter, 2012.
  47. 47.
    A. McCallumzy, K. Nigamy, J. Renniey, K. Seymorey, A machine learning approach to building domain-specific search engines, in 16th International Joint Conference on Artificial Intelligence, IJCAI99, vol. 2 (ACM Digital Library, 1999), pp. 662–667Google Scholar
  48. 48.
    S.K. Bhavnani, Domain-specific search strategies for the effective retrieval of healthcare and shopping information, in CHI 2002, ACM Digital Library (Minneapolis, Minnesota, USA, 2002), pp. 610–611Google Scholar
  49. 49.
    N. Mitsche, Understanding the information search process within a tourism domain-specific search engine, in Information and Communication Technologies in Tourism (Springer, Innsbruck, Austria, 2005), pp 183–193Google Scholar
  50. 50.
    D. Mukhopadhyay, A. Banik, S. Mukherjee, J. Bhattacharya, Y.C. Kim, A domain specific ontology based semantic Web search engine, in 7th International Workshop MSPT 2007 Proceedings (Youngil Publication, Republic of Korea, 2007), pp. 81–89Google Scholar
  51. 51.
    S. Sharma, Information retrieval in domain specific search engine with machine learning approaches. Proc. World Acad. Sci. Eng. Technol. 44, 136–139 (2008)Google Scholar
  52. 52.
    R. Baeza-Yates, M. Ciaramita, P. Mika, H. Zaragoza, Towards semantic search, in 13th International Conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems, vol. 5039 (London, UK, 2008), pp. 4–11Google Scholar
  53. 53.
    B. Fazzinga, G. Gianforme, G. Gottlob, T. Lukasiewicz, Semantic Web search based on ontological conjunctive queries. Web Semant. Sci. Serv. Agents World Wide Web 9, 453–473 (2011). (Elsevier)CrossRefGoogle Scholar
  54. 54.
    W.N. Borst, in Construction of Engineering Ontologies for Knowledge Sharing and Reuse. University of Twente, CTIT Ph.D.-thesis, Series no. 97-14 (1997)Google Scholar
  55. 55.
    J. Heflin, J.A. Hendler, Dynamic ontologies on the web, in Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence (Austin, Texas, USA, 2000), pp. 443–449Google Scholar
  56. 56.
    M. Fernández, I. Cantador, V. López, D. Vallet, P. Castells, E. Motta, Semantically enhanced information retrieval: an ontology-based approach. Web Semant. Sci. Serv. Agents World Wide Web 9(4), 434–452 (2011)CrossRefGoogle Scholar
  57. 57.
    J. Cho, H. Garcia-Molina, L. Page, in Efficient crawling through URL ordering, Technical Report, Computer Science Department, Stanford University, Stanford, CA, USA (1997)Google Scholar
  58. 58.
    R. Nath, S. Bal, A novel mobile crawler system based on filtering off non-modified pages for reducing load on the network. Int. Arab. J. Inf. Technol. 8(3), 272–279 (2011)Google Scholar
  59. 59.
    V. Shkapenyuk, T. Suel, Design and implementation of a high performance distributed Web crawler, in 18th International Conference on Data Engineering (IEEE CS Press, San Jose, CA, 2002), pp. 357–368Google Scholar
  60. 60.
    P. Boldi, B. Codenotti, M. Santini, S. Vigna, Ubicrawler: a scalable fully distributed Web crawler, in 8th Australian World Wide Web Conference, AUSWEB02 (Australia, 2002), pp. 1–14Google Scholar
  61. 61.
    J. Edwards, K.S. McCurley, J.A. Tomlin, An adaptive model for optimizing performance of an incremental Web crawler, in 10th Conference on World Wide Web (Elsevier Science, Hong Kong, 2001), pp. 106–113Google Scholar
  62. 62.
    M. Najork, J.L. Wiener, Breadth-first crawling yields high-quality pages, in 10th Conference on World Wide Web (Elsevier Science, Hong Kong, 2001), pp. 114–118Google Scholar
  63. 63.
    B. Pinkerton, Finding what people want: experiences with the WebCrawler, in 1st World Wide Web Conference (Geneva, Switzerland, 1994)Google Scholar
  64. 64.
    S. Chakrabarti, M. Berg, B.E. Dom, Focused crawling: a new approach to topic-specific Web resource discovery, in 8th International World Wide Web Conference (Elsevier, Toronto, Canada, 1999), pp. 545–562CrossRefGoogle Scholar
  65. 65.
    I.S. Altingovde, O. Ulusoy, Exploiting interclass rules for focused crawling. IEEE Intell. Syst. 19(6), 66–73 (2004). Scholar
  66. 66.
    X.J. Zong, Y. Shen, X.X. Liao, Improvement of HITS for topic-specific Web crawler, in Advances in Intelligent Computing, ICIC 2005, Part I. Lecture Notes in Computer Science, vol. 3644 (2005), pp. 524–532Google Scholar
  67. 67.
    Q. Guo, H. Guo, Z. Zhang, J. Sun, J. Feng, Schema driven and topic specific Web crawling, in Database Systems for Advanced Applications. Lecture Notes in Computer Science, vol. 3453 (2005), pp. 594–599CrossRefGoogle Scholar
  68. 68.
    G. Pant, P. Srinivasan, Link contexts in classifier-guided topical crawlers. IEEE Trans. Knowl. Data Eng. 18(1), 107–122 (2006)CrossRefGoogle Scholar
  69. 69.
    G. Almpanidis, C. Kotropoulos, I. Pitas, Focused crawling using latent semantic indexing—an application for vertical search engines, in Research and Advanced Technology for Digital Libraries. Lecture Notes in Computer Science, vol. 3652 (2005), pp. 402–413CrossRefGoogle Scholar
  70. 70.
    M. Diligenti, F. Coetzee, S. Lawrence, C.L. Giles, M. Gori, Focused crawling using context graphs, in 26th International Conference on Very Large Databases, VLDB (Morgan Kaufmann, San Francisco, 2000), pp. 527–534Google Scholar
  71. 71.
    D. Bergmark, C. Lagoze, A. Sbityakov, Focused crawls, tunneling, and digital libraries, in European Conference on Digital Libraries, ECDL 2002. Lecture Notes in Computer Science, Roma, Italy, vol. 2458 (2002), pp. 91–106Google Scholar
  72. 72.
    H. Zhang, J. Lu, SCTWC: An online semi-supervised clustering approach to topical Web crawlers. Appl. Soft Comput. 10(2), 490–495 (2010). (Elsevier)CrossRefGoogle Scholar
  73. 73.
    A. Patel, N. Schmidt, Application of structured document parsing to focused Web crawling. Comput. Stand. Interfaces 33(3), 325–331 (2011). (Elsevier)CrossRefGoogle Scholar
  74. 74.
    S. Batsakis, E.G.M. Petrakis, E. Milios, Improving the performance of focused Web crawlers. Data Knowl. Eng. 68(10), 1001–1013 (2009). Scholar
  75. 75.
    H. Liu, E. Milios, Probabilistic models for focused Web crawling. Comput. Intell. 28(3), 289–328 (2012)MathSciNetCrossRefGoogle Scholar
  76. 76.
    H. Liu, J. Janssen, E. Millos, Using HMM to learn user browsing patterns for focused Web crawling. Data Knowl. Eng. 59(2), 270–291 (2006). Scholar
  77. 77.
    T. Fu, A. Abbasi, H. Chen, A focused crawler for dark Web forums. J. Am. Soc. Inform. Sci. Technol. 61(6), 1213–1231 (2010). Scholar
  78. 78.
    H. Dong, F.K. Hussain, Focused crawling for automatic service discovery, annotation, and classification in industrial digital ecosystems. IEEE Trans. Industr. Electron. 58(6), 2106–2116 (2011). Scholar
  79. 79.
    J.J. Jung, Towards open decision support systems based on semantic focused crawling. Exp. Syst. Appl. 36(2), 3914–3922 (2009). (Elsevier)CrossRefGoogle Scholar
  80. 80.
    S.Y. Yang, OntoCrawler: a focused crawler with ontology-supported website models for information agents. Exp. Syst. Appl. 37(7), 5381–5389 (2010). (Elsevier)CrossRefGoogle Scholar
  81. 81.
    A. Kundu, R. Dutta, D. Mukhopadhyay, Y.C. Kim, A hierarchical Web page crawler for crawling the internet faster, in The Proceedings of the International Conference on Electronics and Information Technology Convergence (Korea, 2006), pp. 61–67Google Scholar
  82. 82.
    A.A. Fatemeh, S. Ali, An architecture for a focused trend parallel Web crawler with the application of clickstream analysis. Inf. Sci. 184(1), 266–281 (2012). (Elsevier)CrossRefGoogle Scholar
  83. 83.
    J. Cho, H. Garcia-Molina, Parallel crawlers, in 11th International World Wide Web Conference, WWW02 (ACM Digital Library, Honolulu, Hawaii, USA, 2002), 1-58113-449-5/02/0005Google Scholar
  84. 84.
    S. Dong, X. Lu, L. Zhang, A parallel crawling schema using dynamic partition. Lect. Notes Comput. Sci. 3036, 287–294 (2004)CrossRefGoogle Scholar
  85. 85.
    D. Yadav, A.K. Sharma, J.P. Gupta, Parallel crawler architecture and Web page change detection. W. Trans. Comp. 7, 929–940 (2008)Google Scholar
  86. 86.
    J.Y. Lee, S.H. Lee, Scrawler: a seed-by-seed parallel Web crawler, in School of Computing. Soongsil University, Seoul, Korea (2008)Google Scholar
  87. 87.
    S. Ganesh, Ontology based Web crawling—a novel approach, in Advances in Web Intelligence Proceedings. Lecture Notes in Computer Science, vol. 3528 (2005), pp. 140–149CrossRefGoogle Scholar
  88. 88.
    S.Y. Yang, OntoPortal: An ontology-supported portal architecture with linguistically enhanced and focused crawler technologies. Exp. Syst. Appl. 36(6), 10148–10157 (2009). (Elsevier)CrossRefGoogle Scholar
  89. 89.
    P. Spyns, R. Meersman, M. Jarrar, Data modelling versus ontology engineering, in SIGMOD02, Record Special Issue 31(4), 12–17 (2002)CrossRefGoogle Scholar
  90. 90.
    P. Spyns, Y. Tang, R. Meersman, An ontology engineering methodology for DOGMA. J. Appl. Ontol. 5 (2008)Google Scholar
  91. 91.
    N. Tyagi, D. Gupta, A novel architecture for domain specific parallel crawler. Indian J. Comput. Sci. Eng. 1(1), 44–53 (2008)Google Scholar
  92. 92.
    A. Selamat, F. Ahmadi-Abkenari, Architecture for a parallel focused crawler for clickstream analysis, in Proceedings of the Third International Conference on Intelligent Information and Database Systems, ACIIDS11, LNAI, vol 6591 (Daegu, South Korea, 2011), pp. 27–35Google Scholar
  93. 93.
    S. Sinha, R. Dattagupta, D. Mukhopadhyay, A new approach to design a domain specific Web search crawler using multilevel domain classifier, in International Conference on Distributed Computing & Internet Technology, ICDCIT 2013 Proceedings, Bhubaneswar, India. Lecture Notes in Computer Science Series. LNCS, vol. 7753 (Springer, Germany, 2013), pp. 476–487CrossRefGoogle Scholar
  94. 94.
    C. Biemann, Ontology Learning from Text: A Survey of Methods. LDV Forum 20, 75–93 (2005)Google Scholar
  95. 95.
    A. Faatz, S. Hörmann, C. Seeberg, R. Steinmetz, Conceptual enrichment of ontologies by means of a generic and configurable approach, in Proceedings of the European Summer School in Logic, Language and Information Workshop on Semantic Knowledge Acquisition and Categorism, ESSLI01 (Helsinki, Finland, 2001)Google Scholar
  96. 96.
    T. Berners-Lee, J. Hendler, O. Lassila, The semantic web. Sci. Am. 284(5), 34–44 (2001)CrossRefGoogle Scholar
  97. 97.
  98. 98.
    E.M. Tapia, T. Choudhury, M. Philipose, Building reliable activity models using hierarchical shrinkage and mined ontology, in Pervasive Computing. Lecture Notes in Computer Science, vol. 3968 (2006), pp. 17–32Google Scholar
  99. 99.
    Y.S. Hwanga, D.H. Shinb, Y. Kim, Structural change in search engine news service: a social network perspective. Asian J. Commun. 22(2), 160–178 (2012)CrossRefGoogle Scholar
  100. 100.
    H.W. Park, G.A. Barnett, I.Y. Nam, Hyperlink-affiliation network structure of top Web sites: examining affiliates with hyperlink in Korea. J. Am. Soc. Inform. Sci. Technol. 53(7), 592–601 (2002)CrossRefGoogle Scholar
  101. 101.
    K. Norman, J. Chin, The effect of tree structure on search in a hierarchical menu selection system. Behav. Inform. Technol. 7(1), 51–65 (1998)CrossRefGoogle Scholar
  102. 102.
    S. Chakrabarti, B.E. Dom, R. Kumar, P. Raghavan, S. Rajagopalan, A. Tomkins, D. Gibson, J. Kleinberg, Mining the Web’s link structure. IEEE Comput. 32(8), 60–67 (1999)CrossRefGoogle Scholar
  103. 103.
    J. Furnkranz, Exploiting structure information for text classification on the WWW, in Intelligent Data Analysis (1999), pp. 487–498CrossRefGoogle Scholar
  104. 104.
    E.J. Glover, K. Tsioutsiouliklis, S. Lawrence, D.M. Pennock, G.W. Flake, Using Web structure for classifying and describing Web pages, in WWW2002 (Honolulu, Hawaii, USA, 2002)Google Scholar
  105. 105.
    D. Mukhopadhyay, A. Biswas, S. Sinha, A new approach to design domain specific ontology based Web crawler, in 10th International Conference on Information Technology, ICIT 2007 Proceedings, Rourkela, India (IEEE Computer Society Press, California, USA, 2007), pp. 289–291Google Scholar
  106. 106.
    D. Mukhopadhyay, S. Sinha, A new approach to design graph based search engine for multiple domains using different ontologies, in 11th International Conference on Information Technology, ICIT 2008 Proceedings, Bhubaneswar, India (IEEE Computer Society Press, California, USA, 2008), pp. 267–272Google Scholar
  107. 107.
    J. Pitkow, P. Pirolli, Mining longest repeating subsequences to predict World Wide Web surfing, in 2nd USENIX Symposium on Internet Technologies and Systems (Boulder, Colorado, USA, 1999), pp. 139–150Google Scholar
  108. 108.
    I. Zuckerman, W. Albrecht, A. Nicholson, Predicting user’s request on the WWW, in 7th International Conference on User Modeling (Banff, Canada, 1999), pp. 275–284Google Scholar
  109. 109.
    Z. Su, Q. Yang, Y. Lu, H. Zhang, WhatNext: a prediction system for Web requests using N-gram sequence models, in 1st International Conference on Web Information System and Engineering Conference (Hong Kong, China, 2000), pp. 200–207Google Scholar
  110. 110.
    Q. Yang, H.H. Zhang, Integrating Web prefetching and caching using prediction models. World Wide Web J. 4(4), 299–321 (2001) (Kluwer Academic Publishers)CrossRefGoogle Scholar
  111. 111.
    C. Dimopoulos, C. Makris, Y. Panagis, E. Theodoridis, A. Tsakalidis, A Web page usage prediction scheme using sequence indexing and clustering techniques. Data Knowl. Eng. 69(4), 371–382 (2010). (Elsevier)CrossRefGoogle Scholar
  112. 112.
    T. Tian, S.A. Chun, J. Geller, A prediction model for Web search hit counts using word frequencies. J. Inf. Sci. 37(5), 462–475 (2011). Scholar
  113. 113.
    C.H. Lee, Y.I. Lo, Y.H. Fu, A novel prediction model based on hierarchical characteristic of Web site. Exp. Syst. Appl. 38(4), 3422–3430 (2011). (Elsevier)CrossRefGoogle Scholar
  114. 114.
    Google, Case study search engine. (2013)
  115. 115.
    Alltheweb, Case study search engine. (2013)
  116. 116.
    Vivisimo, Case study search engine. (2013)
  117. 117.
    D.J. Zhao, D.L. Lee, A.Q. Luo, Meta-search method with clustering and term correlation, in 9th International Conference on Database Systems for Advances Applications, DASFAA 2004. Lecture Notes in Computer Science (Jeju Island, Korea, 2004), pp. 543–553CrossRefGoogle Scholar
  118. 118.
    R.V. Zwol, H.V. Oostendorp, Google’s “I’m feeling lucky”, truly a gamble?, in Web information systems, WISE 2004. Lecture Notes in Computer Science, vol. 3306 (2004), pp. 378–389Google Scholar
  119. 119.
    V. Diodato, G. Gandt, Back of book indexes and the characteristics of author and nonauthor indexing: report of an exploratory study. J. Am. Soc. Inf. Sci. 42(5), 341–350 (1991)CrossRefGoogle Scholar
  120. 120.
    P.G.B. Enser, Automatic classification of book material represented by back-of-the-book index. J. Doc. 41(3), 135–155 (1985)CrossRefGoogle Scholar
  121. 121.
    F. Leise, Improving usability with a website index, in Boxes and arrows. Available at
  122. 122.
    J.D. Anderson, Guidelines for Indexes and related information retrieval devices, in NISO Technical Report 2, NISO-TR02-1997 (NISO Press, Bethesda, Maryland, 1997)Google Scholar
  123. 123.
    M. Manoj, E. Jacob, Information retrieval on Internet using metasearch engines: a review. J. Sci. Ind. Res. (JSIR) 67(10), 739–746 (2008) (CSIR Publisher)Google Scholar
  124. 124.
    T. Chieueh, K. Gopalan, Improving route lookup performance using net-work processor cache, in ACM/IEEE Conference on Supercomputing (Baltimore, Maryland, USA, 2002)Google Scholar
  125. 125.
    A. Brodnik, S. Carlsson, M. Degermark, S. Pink, Small forwarding tables for fast routing lookups, in Proceedings of ACM SIGCOMM97 (1997)Google Scholar
  126. 126.
    H.J. Chao, Next generation routers. Proc. IEEE 90(9), 1518–1558 (2002)CrossRefGoogle Scholar
  127. 127.
    X. Wang, J. Wu, H. Yang, Robust image retrieval based on color histogram of local feature regions. Multimedia Tools Appl. (Springer, Berlin) 49(2), 323–345 (2010)CrossRefGoogle Scholar
  128. 128.
    C.L. Novak, S.A. Shafer, Anatomy of a color histogram, in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Champaign, 1992), pp. 599–605.
  129. 129.
    C. Clarke, E. Agichtein, S. Dumais, R.W. White, The influence of caption features on clickthrough patterns in Web search, in 30th International Conference on Research and Development in Information Retrieval (ACM SIGIR, New York, 2007), pp. 135–142Google Scholar
  130. 130.
    G. Smith, C. Brien, H. Ashman, Evaluating implicit judgments from image search clickthrough data. J. Am. Soc. Inform. Sci. Technol. 63(12), 2451–2462 (2012)CrossRefGoogle Scholar
  131. 131.
    Z. Su, H. Zhang, S. Li, S. Ma, Relevance feedback content-based image retrieval: Bayesian framework, feature. IEEE Trans. Image Process. 12(8), 924–937 (2003)CrossRefGoogle Scholar
  132. 132.
    B. Luo, X.G. Wang, X.O. Tang, World Wide Web based image search engine using text and image content features, in Proceedings of SPIE Electronic Imaging, vol. 5018 (Santa Clara, 2003), pp. 123–130Google Scholar
  133. 133.
    K.P. Yee, K. Swearingen, K. Li, M. Hearst, Faceted metadata for image search and browsing, in CHI 2003 Proceedings (Fort Lauderdale, 2003), pp. 401–408Google Scholar
  134. 134.
    C.W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Pektovic, P. Yanker, C. Faloutsos, G. Taubin, The QBIC project: querying images by content using color, texture, and shape, in Proceedings of Storage and Retrieval for Image and Video Databases (San Jose, 1993), pp. 173–187Google Scholar
  135. 135.
    D.N.D. Harini, D.L. Bhaskari, Image retrieval system based on feature extraction and relevance feedback, in Proceedings of the CUBE International Information Technology Conference (Pune, 2012), pp. 69–73Google Scholar
  136. 136.
    A.K. Jain, A. Vailaya, Image retrieval using color and shape. Pattern Recogn. 29(8), 1233–1244 (1996). Scholar
  137. 137.
    D. Patra, J. Mridula, Featured based segmentation of color textured images using GLCM and Markov random field model. World Acad. Sci. Eng. Technol. 53(5), 108–113 (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Debajyoti Mukhopadhyay
    • 1
    Email author
  • Sukanta Sinha
    • 1
  • Sukanta Sinha
    • 2
  1. 1.Web Intelligence and Distributed Computing Research Lab, Computer Engineering DepartmentNHITM of Mumbai UniversityKavesar, Thane (W)India
  2. 2.Wipro Limited BrisbaneAustralia

Personalised recommendations