Article

Searching the workplace web

Authors:

Kevin S. McCurley,

John A. Tomlin,

David P. WilliamsonAuthors Info & Claims

WWW '03: Proceedings of the 12th international conference on World Wide Web

Pages 366 - 375

https://doi.org/10.1145/775152.775204

Published: 20 May 2003 Publication History

Abstract

The social impact from the World Wide Web cannot be underestimated, but technologies used to build the Web are also revolutionizing the sharing of business and government information within intranets. In many ways the lessons learned from the Internet carry over directly to intranets, but others do not apply. In particular, the social forces that guide the development of intranets are quite different, and the determination of a "good answer" for intranet search is quite different than on the Internet. In this paper we study the problem of intranet search. Our approach focuses on the use of rank aggregation, and allows us to examine the effects of different heuristics on ranking of search results.

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Cited By

Damas JDevezas JNunes S(2022)Federated Search Using Query Log EvidenceProgress in Artificial Intelligence10.1007/978-3-031-16474-3_64(794-805)Online publication date: 13-Sep-2022
https://doi.org/10.1007/978-3-031-16474-3_64
Mathieu C(2022)Defining knowledge workers' creation, description, and storage practices as impact on enterprise content management strategyJournal of the Association for Information Science and Technology10.1002/asi.2456373:3(472-484)Online publication date: 7-Feb-2022
https://dl.acm.org/doi/10.1002/asi.24563
Lykke MBygholm ASøndergaard LByström K(2021)The role of historical and contextual knowledge in enterprise searchJournal of Documentation10.1108/JD-08-2021-017078:5(1053-1074)Online publication date: 20-Dec-2021
https://doi.org/10.1108/JD-08-2021-0170
Show More Cited By

Index Terms

Searching the workplace web
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

WWW '03: Proceedings of the 12th international conference on World Wide Web

May 2003

772 pages

ISBN:1581136803

DOI:10.1145/775152

Conference Chairs:
Gusztáv Hencsey
MTA SZTAKI, Hungary
,
Bebo White
Stanford Linear Accelerator Center, USA
,
Program Chairs:
Yih-Farn Robin Chen
AT&T Labs -- Research, USA
,
László Kovács
MTA SZTAKI, Hungary
,
Steve Lawrence
Google Inc., USA

Copyright © 2003 ACM.

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Publication History

Published: 20 May 2003

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Cited By

Damas JDevezas JNunes S(2022)Federated Search Using Query Log EvidenceProgress in Artificial Intelligence10.1007/978-3-031-16474-3_64(794-805)Online publication date: 13-Sep-2022
https://doi.org/10.1007/978-3-031-16474-3_64
Mathieu C(2022)Defining knowledge workers' creation, description, and storage practices as impact on enterprise content management strategyJournal of the Association for Information Science and Technology10.1002/asi.2456373:3(472-484)Online publication date: 7-Feb-2022
https://dl.acm.org/doi/10.1002/asi.24563
Lykke MBygholm ASøndergaard LByström K(2021)The role of historical and contextual knowledge in enterprise searchJournal of Documentation10.1108/JD-08-2021-017078:5(1053-1074)Online publication date: 20-Dec-2021
https://doi.org/10.1108/JD-08-2021-0170
Nikolakaki SPitoura ETerzi ETsaparas P(2020)Finding Teams of Maximum Mutual Respect2020 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM50108.2020.00149(1202-1207)Online publication date: Nov-2020
https://doi.org/10.1109/ICDM50108.2020.00149
Hu SWang SHu Z(2020)A Hybrid Model for Online Merchandise Recommendation Based on Ordination and Cluster AnalysisProceedings of the Fourteenth International Conference on Management Science and Engineering Management10.1007/978-3-030-49829-0_27(373-383)Online publication date: 23-Jun-2020
https://doi.org/10.1007/978-3-030-49829-0_27
Xu DShaikh N(2018)A Heuristic Approach for Ranking Items Based on Inputs from Multiple ExpertsInternational Journal of Information Systems and Social Change10.4018/IJISSC.20180701019:3(1-22)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.4018/IJISSC.2018070101
Cleverley PBurnett S(2018)Enterprise search and discovery capability: The factors and generative mechanisms for user satisfactionJournal of Information Science10.1177/0165551518770969(016555151877096)Online publication date: 11-May-2018
https://doi.org/10.1177/0165551518770969
Plachouras V(2018)Field-Based Information Retrieval ModelsEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_927(1471-1476)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_927
Plachouras V(2017)Field-Based Information Retrieval ModelsEncyclopedia of Database Systems10.1007/978-1-4899-7993-3_927-2(1-5)Online publication date: 26-Jan-2017
https://doi.org/10.1007/978-1-4899-7993-3_927-2
Schymik GCorral KSchuff DSt. Louis R(2015)The Benefits and Costs of Using Metadata to Improve Enterprise Document SearchDecision Sciences10.1111/deci.1215446:6(1049-1075)Online publication date: 23-Sep-2015
https://doi.org/10.1111/deci.12154
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