BibSonomy :: publication ::

tag user group author concept BibTeX key search:all search:ewomant

The blue social bookmark and publication sharing system.

(0)

This publication has not been reviewed yet.

rating distribution

average user rating

The average rating is computed over all reviews. However, some of them may be invisible to you due to the visibility setting chosen by the reviewers.

(0.0 of 5.0 based on 0 reviews)

Formal models for expert finding in enterprise corpora

by: Krisztian Balog, Leif Azzopardi, and Maarten de Rijke

In: SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval New York, NY, USA: ACM (2006) , p. 43--50.

Resources (URL, PDF, PS...)

URL:	http://portal.acm.org/citation.cfm?id=1148181
DOI:	10.1145/1148170.1148181

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.

BibTeX record

@inproceedings{1148181,
  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.},
  added-at = {2008-07-01T12:14:45.000+0200},
  address = {New York, NY, USA},
  author = {Balog, Krisztian and Azzopardi, Leif and de Rijke, Maarten},
  biburl = {http://www.bibsonomy.org/bibtex/2a27ce7cb36bd57c450c0ebeedfd04462/ewomant},
  booktitle = {SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval},
  doi = {http://doi.acm.org/10.1145/1148170.1148181},
  interhash = {2c653b9f09eba7cde85e1cda6c1e276f},
  intrahash = {a27ce7cb36bd57c450c0ebeedfd04462},
  isbn = {1-59593-369-7},
  keywords = {enterprise social_software vertical_search},
  location = {Seattle, Washington, USA},
  pages = {43--50},
  publisher = {ACM},
  timestamp = {2008-07-01T12:14:45.000+0200},
  title = {Formal models for expert finding in enterprise corpora},
  url = {http://portal.acm.org/citation.cfm?id=1148181},
  year = 2006
}

Endnote record

%0 Conference Paper
%1 1148181
%A Balog, Krisztian
%A Azzopardi, Leif
%A de Rijke, Maarten
%B SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
%@ 1-59593-369-7
%C New York, NY, USA
%D 2006
%I ACM
%K 
%P 43--50
%T Formal models for expert finding in enterprise corpora
%R http://doi.acm.org/10.1145/1148170.1148181
%U http://portal.acm.org/citation.cfm?id=1148181
%X 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.

BibSonomy :: publication ::

Formal models for expert finding in enterprise corpora

Resources (URL, PDF, PS...)

Abstract

BibTeX record

Endnote record