Aggregation Models for People Finding in Enterprise Corpora

Finding authoritative people of a given field automatically within a large organization is quite helpful in various aspects, such as problem consulting and team building. In this paper, a novel aggregation model is proposed to solve the problem of finding authoritative people. Various kinds of related information in an enterprise repository is assembled to model the knowledge and skills of a candidate, for instance, such information may be the profile which gives a personal description of the candidate, documents related with the candidate, people with similar intellectual structure and so on. Then the candidate is modeled as a multinomial probability distribution over these collected evidence of expertise and candidates are ranked according to the probability of the topic generated by their models. Experimental results on TREC benchmark enterprise corpora demonstrate that our model outperforms current state-of-the-art approaches by a large margin.

[1]  W. Bruce Croft,et al.  Hierarchical Language Models for Expert Finding in Enterprise Corpora , 2006, 2006 18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06).

[2]  Djoerd Hiemstra,et al.  University of Twente at the TREC 2008 Enterprise Track: Using the Global Web as an Expertise Evidence Source , 2008, TREC.

[3]  Jianhan Zhu,et al.  A study of the relationship between ad hoc retrieval and expert finding in enterprise environment , 2008, WIDM '08.

[4]  Nick Craswell,et al.  Overview of the TREC 2006 Enterprise Track , 2006, TREC.

[5]  M. de Rijke,et al.  Formal models for expert finding in enterprise corpora , 2006, SIGIR.

[6]  Chirag Shah,et al.  Evaluating high accuracy retrieval techniques , 2004, SIGIR '04.

[7]  Peter Bailey,et al.  Overview of the TREC 2007 Enterprise Track , 2007, TREC.

[8]  Gianluca Demartini,et al.  Leveraging semantic technologies for enterprise search , 2007, PIKM '07.

[9]  David Hawking,et al.  Challenges in Enterprise Search , 2004, ADC.

[10]  Mark T. Maybury,et al.  Enterprise expert and knowledge discovery , 1999, HCI.

[11]  Nick Craswell,et al.  Overview of the TREC 2005 Enterprise Track , 2005, TREC.

[12]  Yong Yu,et al.  Research on Enterprise Track of TREC 2007 at SJTU APEX Lab , 2007, TREC.

[13]  Thomas H. Davenport,et al.  Book review:Working knowledge: How organizations manage what they know. Thomas H. Davenport and Laurence Prusak. Harvard Business School Press, 1998. $29.95US. ISBN 0‐87584‐655‐6 , 1998 .

[14]  Hongbo Deng,et al.  Formal Models for Expert Finding on DBLP Bibliography Data , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[15]  Peter Bailey,et al.  The CSIRO enterprise search test collection , 2007, SIGF.

[16]  CHENGXIANG ZHAI,et al.  A study of smoothing methods for language models applied to information retrieval , 2004, TOIS.