Architecture of a metasearch engine that supports user information needs

ABSTRACT

When a query is submitted to a metasearch engine, decisions are made with respect to the underlying search engines to be used, what modifications will be made to the query, and how to score the results. These decisions are typically made by considering only the user's keyword query, neglecting the larger information need. Users with specific needs, such as “research papers” or “homepages,” are not able to express these needs in a way that affects the decisions made by the metasearch engine. In this paper, we describe a metasearch engine architecture that considers the user's information need for each decision. Users with different needs, but the same keyword query, may search different sub-search engines, have different modifications made to their query, and have results ordered differently. Our architecture combines several powerful approaches together in a single general purpose metasearch engine.

REFERENCES

• 1: DogPile metasearch engine, http://www, dogpile.com/.
• 2: HotBot search engine, http://www.hotbot.com/.
• 3: Carol L. Barry. The Identification of User Criteria of Relevance and Document Characteristics: Beyond the Topical Approach to Information Retrieval. PhD thesis, Syracuse, 1993.
• 4: Ana B. Benitez , Mandis Beigi , Shih-Fu Chang, Using Relevance Feedback in Content-Based Image Metasearch, IEEE Internet Computing, v.2 n.4, p.59-69, July 1998 [doi>10.1109/4236.707692]
• 5: Susan Gauch, Guihun Wang, and Mario Gomez. Pro- Fusion: Intelligent fusion from multiple, distributed search engines. Journal of Universal Computer Science, 2(9), 1996.
• 6: Eric J. Glover , William P. Birmingham, Using decision theory to order documents, Proceedings of the third ACM conference on Digital libraries, p.285-286, June 23-26, 1998, Pittsburgh, Pennsylvania, United States [doi>10.1145/276675.276732]
• 7: Eric J. Glover, William P. Birmingham, and Michael D. Gordon. Improving web search using utility theory. In Web Information and Data Management (WIDM'98), pages 5-8, Bethesda, MD, 1998. ACM Press.
• 8: Adele E. Howe and Daniel Dreilinger. SavvySearch: A meta-search engine that learns which search engines to query. AI Magazine, 18(2), 1997.
• 9: Ralph L. Keeney and Howard Raiffa. Decisions with Multiple Objectives. John Wiley and Sons, New York, 1976.
• 10: Steve Lawrence , C. Lee Giles, Context and Page Analysis for Improved Web Search, IEEE Internet Computing, v.2 n.4, p.38-46, July 1998 [doi>10.1109/4236.707689]
• 11: Steve Lawrence , C. Lee Giles, Inquirus, the NECI meta search engine, Proceedings of the seventh international conference on World Wide Web 7, p.95-105, April 1998, Brisbane, Australia
• 12: Steve Lawrence and C. Lee Giles. Accessibility of information on the web. Nature, 400(July 8): 107-109, 1999.
• 13: Michael L. Mauldin. Lycos: Design choices in an Internet search service. IEEE Expert, (January- February):8-11, 1997.
• 14: Hien Nguyen and Peter Haddawy. The Decision- Theoretic Video Advisor. In AAAI Workshop on Recommender Systems, 1998.
• 15: Linda Schamber , Michael Eisenberg , Michael S. Nilan, A re-examination of relevance: toward a dynamic, situational definition, Information Processing and Management: an International Journal, v.26 n.6, p.755-776, 1990 [doi>10.1016/0306-4573(90)90050-C]
• 16: E. Selberg and O. Etzioni. The MetaCrawler architecture for resource aggregation on the Web. IEEE Expert, (January-February): 11-14, 1997.

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.1 MODELS AND PRINCIPLES
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Query formulation
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)

General Terms:
Design, Human Factors, Management, Performance, Theory