CoSeNa: a context-based search and navigation system

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.
• 2: D. Ellis. A behavioral approach to information retrieval system design. J. Doc., 45(3):171--212, 1989.
• 3: Fellbaum. WordNet: An Electronic Lexical Database. The MIT Press, May 1998.
• 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.
• 5: F. A. Grootjen and T. P. van der Weide. Conceptual query expansion. Data Knowl. Eng., 56(2):174--193, 2006.
• 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.
• 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.
• 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.
• 9: Y. Qiu and H.-P. Frei. Concept based query expansion. In SIGIR '93, pages 160--169. ACM.
• 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.
• 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.
• 12: G. M. Sacco. Dynamic taxonomies: A model for large information bases. IEEE TKDE, 12(3):468--479, 2000.
• 13: G. Salton and C. Buckley. Term-weighting approaches in automatic text retrieval. In Information Processing and Management, pages 513--523, 1988.
• 14: S. P. Shashank. Navigation-aided retrieval. In Proc of WWW'07, pages 391--400.
• 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.
• 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.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.4 Hypertext/Hypermedia

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
      H.3.7 Digital Libraries

General Terms:
Algorithms, Experimentation

Keywords:
HCI, data knowledge management, web based digital ecosystems