Skip to main content
Log in

A conceptual model for user-centered quality information retrieval on the World Wide Web

  • Published:
Journal of Intelligent Information Systems Aims and scope Submit manuscript

Abstract

Information retrieval from the Internet is becoming a commonplace phenomenon. Users and consumers are browsing websites and seeking various kinds of information for personal use. Retrieving quality information from the Internet can be challenging even for the computer-savvy. There are several search engines, even some personalized, to help users search for information on the Internet. In spite of all the claims about search engines, users still have difficult time retrieving relevant information quickly. This paper proposes a general conceptual model for user-centered quality information retrieval (UCQIR) from the Internet. The UCQIR conceptual model is presented in an architectural form. The UCQIR architectural model uses the concept of “Task-performer” to present various aspects of an information retrieval system at the knowledge level. Task-performer is an abstract construct used to conceptualize the idea of an entity that is competent in doing its tasks. The UCQIR architectural model can be used to easily design and develop domain-specific, user-centered quality information retrieval systems. The proposed UCQIR conceptual model is unique and comprehensive. The use of the conceptual model is illustrated through a design of a patient-centered quality medical information retrieval for the medical domain. We also present an experimental evaluation of a UCQIR prototype based upon real user experiences. The experimental results are very positive.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Adar, E., Karger, D., & Stein, L. A. (1999). Haystack: Per-user information environments. CIKM, Kansas City, MO, 2–6 November.

  • Allan, J., et al. (2003). Challenges in information retrieval and language modeling. SIGIR Forum, 37(1), 31-47.

    Article  MathSciNet  Google Scholar 

  • Ambler, S. W. (2008). The agile unified process. http://www.ambysoft.com/unifiedprocess/agileUP.html.

  • Apostolico, A., & Yates, R. (2006). Advances in information retrieval: An introduction to the special issue. Information Systems, 31, 569–572.

    Article  Google Scholar 

  • Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval. New York: Addison Wesley, ACM.

    Google Scholar 

  • Baldwin, D., & Yadav, S. B. (1995). The process of research investigation in artificial intelligence—a unified view. IEEE Transactions on Systems, Man, and Cybernetics, 25(5), 852–861.

    Article  Google Scholar 

  • Belkin, N., & Croft, W. (1992). Information filtering and information retrieval: Two sides of the same coin? Communications of the ACM, 35(12), 29–38.

    Article  Google Scholar 

  • Berners-Lee, T. (1998). Semantic web roadmap. World wide web consortium (W3C). http://www.w3.org/DesignIssues/Semantic.html.

  • Bharat, K. (2000). SearchPad: Explicit capture of search context to support web search. In Proceedings of 9th international WWW conference. Amsterdam.

  • Bharat, K., & Henzinger, M. (1998). Improved algorithms for topic distillation in a hyperlinked environment. In The 21st ACM SIGIR conference on research and development in information retrieval; SIGIR’98.

  • Bingi, R. P., Khazanchi, D., & Yadav, S. B. (1995). A framework for the comparative analysis and evaluation of knowledge representation schemes. Journal of Information Processing and Management, 31(2), 233–247.

    Article  Google Scholar 

  • Brooks, H. M. (1987). Expert systems and intelligent information retrieval. Information Processing & Management, 23(4), 367–382.

    Article  Google Scholar 

  • Callan, J. P., Lu, Z., & Croft, W. B. (1995). Searching distributed collections with inference networks. In Proceedings of the 18th annual international SIGIR conference (pp. 21–28). Seattle, Washington.

  • Chaffee, J., & Gauch, S. (2000). Personal ontology for web navigation. In Proceedings 9th intl. conference on information and knowledge management (CIKM’00) McLean VA (pp. 227–234).

  • Chakrabarti, S., Berg, M., & Dom, B. (1999). Focused crawling: A new approach to topic-specific web resource discovery. Amsterdam: Elsevier.

    Google Scholar 

  • Chuang, T.-T., & Yadav, S. (1998). Development of an adaptive decision support systems. Decision Support Systems, 24, 73–87.

    Article  Google Scholar 

  • Chowdhury, G. G. (2004). Introduction to modern information retrieval (2nd ed.). London: Facet.

    Google Scholar 

  • Dalal, N. P., & Yadav, S. (1992). The design of a knowledge-based decision support system to support an information analyst in determining requirements, special issue on expert systems and decision support systems of the decision sciences (Vol. 23, no. 6).

  • Dai, H., & Mobasher, B. (2005). Integrating semantic knowledge with web usage mining for personalization. In A. Scime (Ed.), A book chapter (chapter 13) in web mining: Applications and techniques. Hershey: Idea Group.

    Google Scholar 

  • Eguchi, K. (2000). Incremental query expansion using local information of clusters. In Proceedings of the 4th world multiconference on systemics; cybernetics and informatics (SCI 2000) (Vol. 2, pp. 310–316).

  • eEurope (2002). Quality criteria for health related websites. Journal of Medical Internet Research 2002, 4(3):e15. URL:http://www.jmir.org/2002/3/e15/.

  • Finkelstein, L., et al. (2002). Placing search in context: The concept revisited. ACM Transactions on Information Systems, 21(1), 116–131.

    Article  MathSciNet  Google Scholar 

  • Foltz, P., & Dumais, S. (1992). Personalized information delivery: An analysis of information filtering methods. Communications of the ACM, 35(12), 51–60.

    Article  Google Scholar 

  • Gravano, L., Garcia-Molina, H., & Tomasic, A. (1999). GlOSS: Text-source discovery over the internet. ACM Transactions on Database Systems, 24(2), 229–264.

    Article  Google Scholar 

  • Gupta, S., Kaiser, G., et al. (2004). Automating content extraction of HTML documents. Dordrecht: Kluwer.

    Google Scholar 

  • Hersh, W. (2003). Information retrieval: A health and biomedical perspective (2nd ed.). Berlin: Springer.

    Google Scholar 

  • Hevner, A., March, S. T., Park, J., & Ram, S. (2004). Design science research. MIS Quarterly, 28(1), 75–105.

    Google Scholar 

  • Hoover, J. N. (2007). The ultimate answer machine. A news article. InformationWeek (pp. 41–47). 6 August 2007.

  • IEEE Standards (2004). IEEE STD 1471–2000 IEEE recommended practice for architectural description of software-intensive systems—description.

  • James, M. (2002). Structured knowledge source integration and its applications to information fusion. In Proceedings of the fifth international conference on information fusion. Annapolis, MD.

  • Ko, I., Neches, R., & Yao, K. (2000). Semantically-based active document collection templates for web information management systems. In Proceedings of the ECDL 2000 workshop on the semantic web. Lisbon, Portugal.

  • Ko, I., Yao, K., & Neches, R. (2002). Dynamic coordination of information management services for processing dynamic web content. In WWW, 7–11 May 2002. Honolulu, Hawaii, USA.

  • Leroy, G., Tolle, K. M., & Chen, H. (1999). Customizable and ontology-enhanced medical information retrieval interfaces. In Proceedings international medical informatics association workgroup 6 on medical concept representation. Phoenix, Arizona.

  • Li, W., Geng, Z., Li, Y., & Xu, Z. (2004). Ontology-based intelligent information retrieval system. In Canadian conference on electrical and computer engineering (CCECE) (pp. 373–376). Niagara Falls.

  • Liu, L. (1999). Query routing in large-scale digital library systems. In International conference on data engineering (ICDE’99) (pp. 154–163). Sydney: IEEE Press.

    Google Scholar 

  • Matuszek, C., Cabral, J., Witbrock, M., & DeOliveira, J. (2006). An introduction to the syntax and content of cyc. In Proceedings of the 2006 AAAI spring symposium on formalizing and compiling background knowledge and its applications to knowledge representation and question answering. Stanford, CA.

  • Medical World Search (1997). http://www.mwsearch.com/goals.html.

  • Mitra, M., Singhal, A., & Buckley, C. (1998). Improving automatic query expansion. In Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval (pp. 206–214). Melbourne, Australia.

  • Newell, A. (1981). The knowledge level. AI Magazine, 2, 1–19.

    MathSciNet  Google Scholar 

  • Newell, A. (1982). The knowledge level. Artificial Intelligence, 18, 87–127.

    Article  Google Scholar 

  • Newell, A. (1990). Unified theories of cognition. Cambridge: Harvard University Press.

    Google Scholar 

  • NLM (2005). Unified Modeling Language Knowledge Sources. Developer’s Guide.

  • Parent, S., Mobasher, B., & Lytinen, S. (2001). An adaptive agent for web exploration based on concept hierarchies. In Proceedings of the ninth international conference on human computer interaction. New Orleans: LA.

    Google Scholar 

  • Park, J.-M. (1998). Intelligent query and browsing information retrieval(iqbir) agent. In IEEE international conference on acoustics, speech, and signal processing.

  • Parker, K. (1995). A holistic profile for information filtering systems. Unpublished Ph.D. dissertation. Lubbock, Texas: Texas Tech University.

  • Pitkow, J., et al. (2002). Personalized search. Communications of the ACM, 45(9), 50–55.

    Article  Google Scholar 

  • Pratt, W., & Sim, I. (1995). Physician’s information customizer: Using a shareable user model to filter the medical literature. In Proceedings of the international conference on medical informatics (MEDINFO’95).

  • Pretschner, A., & Gauch, S. (1999). Ontology based personalized search. In Proceedings 11th IEEE intl. conference on tools with artificial intelligence (ICTAI’99) (pp. 391–398). Chicago, IL.

  • Shaw, N. G., Mian, A., & Yadav, S. B. (2002). A comprehensive agent-based architecture for intelligent information retrieval in a distributed heterogeneous environment. Decision Support Systems, 32, 401–415.

    Article  Google Scholar 

  • Sieg, A., Mobasher, B., Lytinen, S., & Burke, R. (2003). Concept based query enhancement in the arch search agent. In Proceedings of the 4th international conference on internet computing. Las Vegas, NV.

  • Simon, H. A. (1981). The sciences of the artificial (2nd ed.). Cambridge: MIT Press.

    Google Scholar 

  • Suarez, H., Hao, X., & Chang, I. (1997). Searching for information on the internet using the UMLS and medical world search. In Proceedings of the american medical informatics association; fall symposium (pp. 824–828).

  • Sugiura, A, & Etzioni, O. (2000). Query routing for web search engines: Architecture and experiments. Computer Networks, 33, 417–429.

    Article  Google Scholar 

  • Tawil, A.-R., & Behrendt, W. (1997). Requirements for components of an intelligent information retrieval model for the WWW. The Institution of Electrical Engineers; Savoy Place, London, UK.

  • Tu, H.-C., & Hsiang, J. (1998). An architecture and category knowledge for intelligent information retrieval agents. In Proceedings of the thirty-first hawaii international conference on system sciences (Vol. 4, pp. 405–414).

  • W3C-1 (2001). Semantic web. http://www.w3.org/2001/sw/.

  • W3C-2 (2004). RDF. http://www.w3.org/RDF/.

  • W3C-3 (2005). DOM. http://www.w3.org/DOM/.

  • W3C-4 (1999). Resource description framework (RDF) model and syntax. http://www.w3.org/TR/REC-rdf.syntax/.

  • Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490–495.

    Article  Google Scholar 

  • Xu, J., & Croft, W. B. (1996). Query expansion using local and global document analysis. In Proceedings of the nineteenth annual international acm sigir conference on research and development in information retrieval (pp. 4–11).

  • Yadav, S. B. (2008). Automation of webpage quality determination. International Journal of Information Quality, 2(2), 152–176.

    Article  Google Scholar 

  • Yadav, S. B., & Bellah, J. (2006). An improved method for automatically determining webpage cohesiveness for quality information retrieval from world wide web. In Proceedings of the 11th international conference on information quality. Cambridge, MA, 10–12 November 2006.

  • Yao, Y. (2002). Information retrieval support systems. In Proceedings of FUZZ-IEEE’02 (pp. 773–778).

  • Zhu, X., & Gauch, S. (2000). Incorporating quality metrics in centralized/distributed information retrieval on the world wide web. In SIGIR 2000. Athens, Greece.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Surya B. Yadav.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yadav, S.B. A conceptual model for user-centered quality information retrieval on the World Wide Web. J Intell Inf Syst 35, 91–121 (2010). https://doi.org/10.1007/s10844-009-0090-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10844-009-0090-y

Keywords

Navigation