skip to main content
10.1145/1643823.1643864acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmedesConference Proceedingsconference-collections
research-article

CoSeNa: a context-based search and navigation system

Published:27 October 2009Publication History

ABSTRACT

Most of the existing document and web search engines rely on keyword-based queries. To find matches, these queries are processed using retrieval algorithms that rely on word frequencies, topic recentness, document authority, and (in some cases) available ontologies. In this paper, we propose an innovative approach to exploring text collections using a novel keywords-by-concepts (KbC) graph, which supports navigation using domain-specific concepts as well as keywords that are characterizing the text corpus. The KbC graph is a weighted graph, created by tightly integrating keywords extracted from documents and concepts obtained from domain taxonomies. Documents in the corpus are associated to the nodes of the graph based on evidence supporting contextual relevance; thus, the KbC graph supports contextually informed access to these documents. In this paper, we also present CoSeNa (Context-based Search and Navigation) system that leverages the KbC model as the basis for document exploration and retrieval as well as contextually-informed media integration.

References

  1. M. J. Bates. The design of browsing and berrypicking techniques for the online search interface. Online Review, 13(5):407--424, 1989.Google ScholarGoogle ScholarCross RefCross Ref
  2. D. Ellis. A behavioral approach to information retrieval system design. J. Doc., 45(3):171--212, 1989. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Fellbaum. WordNet: An Electronic Lexical Database. The MIT Press, May 1998.Google ScholarGoogle Scholar
  4. S. Gauch, J. Chaffee, and A. Pretschner. Ontology-based personalized search and browsing. Web Intelli. and Agent Sys., 1(3--4):219--234, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. F. A. Grootjen and T. P. van der Weide. Conceptual query expansion. Data Knowl. Eng., 56(2):174--193, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. J. W. Kim and K. S. Candan. Cp/cv: concept similarity mining without frequency information from domain describing taxonomies. In CIKM '06, pages 483--492, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. W.-S. Li and K. S. Candan. Semcog: A hybrid object-based image and video database system and its modeling, language, and query processing. TAPOS, 5(3):163--180, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. R. Mandala, T. Tokunaga, and H. Tanaka. Combining multiple evidence from different types of thesaurus for query expansion. In Proc of ACM SIGIR'99, pages 191--197, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Y. Qiu and H.-P. Frei. Concept based query expansion. In SIGIR '93, pages 160--169. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. S. Y. Rieh and H. Xie. Analysis of multiple query reformulations on the web: the interactive information retrieval context. Inf. Process. Manage., 42(3):751--768, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. I. Ruthven and M. Lalmas. A survey on the use of relevance feedback for information access systems. Knowl. Eng. Rev., 18(2):95--145, June 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. G. M. Sacco. Dynamic taxonomies: A model for large information bases. IEEE TKDE, 12(3):468--479, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. In Information Processing and Management, pages 513--523, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. S. P. Shashank. Navigation-aided retrieval. In Proc of WWW'07, pages 391--400. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. A. Spink, R. I. Building, D. Wolfram, and T. Saracevic. Searching the web: the public and their queries. J. of the American Society for Information Science and Technology, 52:226--234, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. Teevan, C. Alvarado, M. S. Ackerman, and D. R. Karger. The perfect search engine is not enough: a study of orienteering behavior in directed search. In SIGCHI'04, pages 415--422. ACM, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. CoSeNa: a context-based search and navigation system

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Other conferences
              MEDES '09: Proceedings of the International Conference on Management of Emergent Digital EcoSystems
              October 2009
              525 pages
              ISBN:9781605588292
              DOI:10.1145/1643823

              Copyright © 2009 ACM

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 27 October 2009

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • research-article

              Acceptance Rates

              Overall Acceptance Rate267of682submissions,39%

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader