Learning search pattern for construction procurement using keyword net

Ren-Jye Dzeng, Shyh Shiuh Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As more and more procurement websites become available on the Internet, seeking information from websites has become an essential part of a contractor's procurement undertaking. Several e-markets, specifically for construction, have also been established, including bLiquid.com and ProcureZone. However, most websites provide only two primary ways of searching for information, namely by index/menu or by keyword. Instead of relying on the primitive search engines found in most procurement websites, a search guide system could help a user's keyword search by reducing the number of keywords required to find the desired information. Our research recognized that professional procurement experience helped users more effectively carry out website information searches, by using fewer keywords. We planned to capture such experience in order to guide inexperienced users in their search. The research goal was to improve search effectiveness by guiding the user's search using three approaches; namely correction, specification and extension. Based on these three approaches, this research applied the following guides: correction; specification-by-equivalence; specification-by-detail; extension-by-time; extension-by-location; extension-by-team; and extension-by-component. The paper will describe how we classified users for learning credibility, and the learning framework for recording expert users' search patterns. Twelve professionals, using 14 procurement packages, with 64 items in total, evaluated the proposed framework. It will be demonstrated that the proposed learning keyword guide facilitated a dynamic, customized menu and indexing system, and reduced the number of keywords required for the professionals to find the information they desired.

Original languageEnglish
Title of host publicationArtificial Intelligence Applications and Innovations - FIP TCI2 WG12.5 - 2nd IFIP Conference on Artificial Intelligence Applications and Innovations, AIAI 2005
PublisherSpringer New York LLC
Pages69-78
Number of pages10
ISBN (Print)9780387283180
DOIs
StatePublished - 1 Jan 2005
EventIFIP TCI2 WG12.5 2nd IFIP Conference on Artificial Intelligence Applications and Innovations, AIAI 2005 - Beijing, China
Duration: 7 Sep 20059 Sep 2005

Publication series

NameIFIP Advances in Information and Communication Technology
Volume187
ISSN (Print)1868-4238

Conference

ConferenceIFIP TCI2 WG12.5 2nd IFIP Conference on Artificial Intelligence Applications and Innovations, AIAI 2005
CountryChina
CityBeijing
Period7/09/059/09/05

Keywords

  • Construction procurement
  • E-commerce
  • Information search
  • Knowledge acquisition
  • Machine learning

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  • Cite this

    Dzeng, R-J., & Wang, S. S. (2005). Learning search pattern for construction procurement using keyword net. In Artificial Intelligence Applications and Innovations - FIP TCI2 WG12.5 - 2nd IFIP Conference on Artificial Intelligence Applications and Innovations, AIAI 2005 (pp. 69-78). (IFIP Advances in Information and Communication Technology; Vol. 187). Springer New York LLC. https://doi.org/10.1007/0-387-29295-0_8