Detecting and supporting known item queries in online public access catalogs

ABSTRACT

When users seek to find specific resources in a digital library, they often use the library catalog to locate them. These catalog queries are defined as known item queries. As known item queries search for specific resources, it is important to manage them differently from other search types, such as area searches. We study how to identify known item queries in the context of a large academic institution's online public access catalog (OPAC), in which queries are issued via a simple keyword interface. We also examine how to recognize when a known item query has retrieved the item in question. Our approach combines techniques in machine learning, language modeling and machine translation evaluation metrics to build a classifier capable of distinguishing known item queries and correctly classifies titles for whether they are the known item sought with an 80% and 95% correlation to human performance, respectively on each task. To our knowledge, this is the first report of such work, which has the potential to streamline the user interface of both OPACs and digital libraries in support of known item searches.

REFERENCES

• 1: B. Allen. Recall Cues in Known-Item Retrieval. J. Amer. Society for Info. Science. 40(4). 1989. pp. 246--252
• 2: Marcia J. Bates, Where should the person stop and the information search interface start?, Information Processing and Management: an International Journal, v.26 n.5, p.575-591, 1990 [doi>10.1016/0306-4573(90)90103-9]
• 3: J. Bollen and R. Luce. Evaluation of Digital Library Impact and User Communities by Analysis of Usage Patterns, D-Lib Magazine, June 2002.
• 4: D. Harman (ed.) The Fourth Text REtrieval Conference (TREC-4). NIST Special Publication 500--236. 1995.
• 5: Innovative Interfaces - Innopac http://www.iii.com/
• 6: K. Jarvelin and P. Ingwersen. Information seeking research needs extension towards tasks and technology. Info. Research. 10(1). 2004.
• 7: Frederick Jelinek, Statistical methods for speech recognition, MIT Press, Cambridge, MA, 1998
• 8: Frederick G. Kilgour, Known-item online searches employed by scholars using surname plus first, or last, or first and last title words, Journal of the American Society for Information Science and Technology, v.52 n.14, p.1203-1209, December 2001 [doi>10.1002/asi.1186]
• 9: Frederick G. Kilgour , Barbara B. Moran , John R. Barden, Retrieval effectiveness of surname-title-word searches for known items by academic library users, Journal of the American Society for Information Science, v.50 n.3, p.265-270, March 1999 [doi>10.1002/(SICI)1097-4571(1999)50:3<265::AID-ASI9>3.3.CO;2-I]
• 10: R. Larson. The Decline of Subject Searching: Long-Term Trends and Patterns of Index Use in an Online Catalog. J. of Amer. Society for Info. Science. 42(3). 1991. pp. 197--215
• 11: M.-K. Leong. Concrete Queries in Specialized Domains: Known Item as Feedback for Query Formulation. In Proc. of TREC-6, NIST Special Publication 500--240.
• 12: National Institute of Science and Technology. Readme documentation for the MT evaluation kit, version 10. 2003. Available from: http://www.nist.gov/speech/tests/mt/resources/scoring.htm
• 13: Paul Ogilvie , Jamie Callan, Combining document representations for known-item search, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860463]
• 14: Kishore Papineni , Salim Roukos , Todd Ward , Wei-Jing Zhu, BLEU: a method for automatic evaluation of machine translation, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, July 07-12, 2002, Philadelphia, Pennsylvania [doi>10.3115/1073083.1073135]
• 15: Fuchun Peng , Dale Schuurmans , Shaojun Wang, Language and task independent text categorization with simple language models, Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, p.110-117, May 27-June 01, 2003, Edmonton, Canada [doi>10.3115/1073445.1073470]
• 16: A. Ratnaparkhi. A Maximum Entropy Part-Of-Speech Tagger, In Proc. of the Empirical Methods in Natural Language Processing Conference (EMNLP '96). Pennsylvania, USA. 1996.
• 17: Daniel E. Rose , Danny Levinson, Understanding user goals in web search, Proceedings of the 13th international conference on World Wide Web, May 17-20, 2004, New York, NY, USA [doi>10.1145/988672.988675]
• 18: Amanda Spink , Dietmar Wolfram , Major B. J. Jansen , Tefko Saracevic, Searching the Web: the public and their queries, Journal of the American Society for Information Science and Technology, v.52 n.3, p.226-234, Feb. 1, 2001 [doi>10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.3.CO;2-I]
• 19: Debra J. Slone, Encounters with the OPAC: on-line searching in public libraries, Journal of the American Society for Information Science, v.51 n.8, p.757-773, May 2000 [doi>10.1002/(SICI)1097-4571(2000)51:8<757::AID-ASI80>3.3.CO;2-K]
• 20: Ian H. Witten , Eibe Frank, Data mining: practical machine learning tools and techniques with Java implementations, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2000

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

Additional Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.m Miscellaneous

General Terms:
Algorithms, Experimentation, Human Factors

Keywords:
known item queries, query language model, query types