Article

Formal models for expert finding in enterprise corpora

Authors:

Krisztian Balog,

Leif Azzopardi,

Maarten de RijkeAuthors Info & Claims

SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 43 - 50

https://doi.org/10.1145/1148170.1148181

Published: 06 August 2006 Publication History

Abstract

Searching an organization's document repositories for experts provides a cost effective solution for the task of expert finding. We present two general strategies to expert searching given a document collection which are formalized using generative probabilistic models. The first of these directly models an expert's knowledge based on the documents that they are associated with, whilst the second locates documents on topic, and then finds the associated expert. Forming reliable associations is crucial to the performance of expert finding systems. Consequently, in our evaluation we compare the different approaches, exploring a variety of associations along with other operational parameters (such as topicality). Using the TREC Enterprise corpora, we show that the second strategy consistently outperforms the first. A comparison against other unsupervised techniques, reveals that our second model delivers excellent performance.

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Cited By

de Campos LFernández-Luna JHuete JRibadas-Pena FBolaños N(2024)Information Retrieval and Machine Learning Methods for Academic Expert FindingAlgorithms10.3390/a1702005117:2(51)Online publication date: 23-Jan-2024
https://doi.org/10.3390/a17020051
Schoegje THardman LDe Vries APieters T(2024)Improving expert search effectiveness: Comparing ways to rank and present search resultsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638296(56-65)Online publication date: 10-Mar-2024
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Index Terms

Formal models for expert finding in enterprise corpora
1. Information systems

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Published In

cover image ACM Conferences

SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval

August 2006

768 pages

ISBN:1595933697

DOI:10.1145/1148170

General Chair:
Efthimis N. Efthimiadis
University of Washington
,
Program Chairs:
Susan Dumais
Microsoft Research, Redmond
,
David Hawking
CSIRO ICT Centre, Canberra, Australia
,
Kalervo Järvelin,
University of Tampere, Finland

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 August 2006

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SIGIR06: The 29th Annual International SIGIR Conference

August 6 - 11, 2006

Washington, Seattle, USA

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

de Campos LFernández-Luna JHuete JRibadas-Pena FBolaños N(2024)Information Retrieval and Machine Learning Methods for Academic Expert FindingAlgorithms10.3390/a1702005117:2(51)Online publication date: 23-Jan-2024
https://doi.org/10.3390/a17020051
Schoegje THardman LDe Vries APieters T(2024)Improving expert search effectiveness: Comparing ways to rank and present search resultsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638296(56-65)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638296
Wang YLiu JXu XKe XWu TGou X(2023)Efficient and Effective Academic Expert Finding on Heterogeneous Graphs through (k, 𝒫)-Core based EmbeddingACM Transactions on Knowledge Discovery from Data10.1145/357836517:6(1-35)Online publication date: 22-Mar-2023
https://dl.acm.org/doi/10.1145/3578365
Li LYan MTao ZChen HWu X(2023)Semi-Supervised Graph Pattern Matching and Rematching for Expert Community LocationACM Transactions on Knowledge Discovery from Data10.1145/353262317:1(1-26)Online publication date: 20-Feb-2023
https://dl.acm.org/doi/10.1145/3532623
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Mustafa GRauf AAl-Shamayleh AAhmed BAlrawagfeh WAfzal MAkhunzada A(2023)Exploring the Significance of Publication-Age-Based Parameters for Evaluating Researcher ImpactIEEE Access10.1109/ACCESS.2023.330401311(86597-86610)Online publication date: 2023
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Mustafa GRauf AAhmed BAfzal MAkhunzada AAlharthi S(2023)Comprehensive Evaluation of Publication and Citation Metrics for Quantifying Scholarly InfluenceIEEE Access10.1109/ACCESS.2023.329091711(65759-65774)Online publication date: 2023
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Bayraktar MKaya M(2023)Author-Profile-Based Journal Recommendation for a Candidate Article: Using Hybrid Semantic Similarity and Trend AnalysisIEEE Access10.1109/ACCESS.2023.327173211(45826-45837)Online publication date: 2023
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Alduais AAl-Khulaidi MAllegretta SAbdulkhalek M(2023)Forensic linguistics: A scientometric reviewCogent Arts & Humanities10.1080/23311983.2023.221438710:1Online publication date: 23-May-2023
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