Non-local evidence for expert finding

ABSTRACT

The task addressed in this paper, finding experts in an enterprise setting, has gained in importance and interest over the past few years. Commonly, this task is approached as an association finding exercise between people and topics. Existing techniques use either documents (as a whole) or proximity-based techniques to represent candidate experts. Proximity-based techniques have shown clear precision-enhancing benefits. We complement both document and proximity-based approaches to expert finding by importing global evidence of expertise, i.e., evidence obtained using information that is not available in the immediate proximity of a candidate expert's name occurrence or even on the same page on which the name occurs. Examples include candidate priors, query models, as well as other documents a candidate expert is associated with.

Using the CERC data set created for the TREC 2007 Enterprise track we identify examples of non-local evidence of expertise. We then propose modified expert retrieval models that are capable of incorporating both local (either document or snippet-based) evidence and non-local evidence of expertise. Results show that our refined models significantly outperform existing state-of-the-art approaches.

REFERENCES

• 1: Peter Bailey , Nick Craswell , Ian Soboroff , Arjen P. de Vries, The CSIRO enterprise search test collection, ACM SIGIR Forum, v.41 n.2, p.42-45, December 2007 [doi>10.1145/1328964.1328969]
• 2: P. Bailey, N. Craswell, A. P. de Vries, and I. Soboroff. Overview of the TREC 2007 Enterprise Track. In The Sixteenth Text REtrieval Conference Proc. (TREC 2007), 2008.
• 3: K. Balog. People Search in the Enterprise. PhD thesis, University of Amsterdam, 2008.
• 4: K. Balog and M. de Rijke. Associating people and documents. In Proc. 30th European Conference on Information Retrieval, pages 296--308, 2008.
• 5: Krisztian Balog , Leif Azzopardi , Maarten de Rijke, Formal models for expert finding in enterprise corpora, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148181]
• 6: Krisztian Balog , Toine Bogers , Leif Azzopardi , Maarten de Rijke , Antal van den Bosch, Broad expertise retrieval in sparse data environments, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, July 23-27, 2007, Amsterdam, The Netherlands [doi>10.1145/1277741.1277836]
• 7: Krisztian Balog , Wouter Weerkamp , Maarten de Rijke, A few examples go a long way: constructing query models from elaborate query formulations, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, July 20-24, 2008, Singapore, Singapore [doi>10.1145/1390334.1390399]
• 8: K. Balog, L. Azzopardi, and M. de Rijke. A language modeling framework for expertise search. In Information Processing & Management, To Appear.
• 9: Y. Cao, J. Liu, S. Bao, and H. Li. Research on Expert Search at Enterprise Track of TREC 2005. In The Fourteenth Text REtrieval Conference Proceedings (TREC 2005), 2006.
• 10: N. Craswell, D. Hawking, A. M. Vercoustre, and P. Wilkins. P@noptic expert: Searching for experts not just for documents. In Ausweb, 2001.
• 11: N. Craswell, A. P. de Vries, and I. Soboroff. Overview of the TREC-2005 Enterprise Track. In The Fourteenth Text REtrieval Conference Proceedings (TREC 2005), 2006.
• 12: H. Fang and C. Zhai. Probabilistic models for expert finding. In Proc. 29th European Conference on Information Retrieval, pages 418--430, 2007.
• 13: Victor Lavrenko , W. Bruce Croft, Relevance based language models, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.120-127, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383972]
• 14: C. Macdonald and I. Ounis. A belief network model for expert search. In Proceedings of 1st conference on Theory of Information Retrieval (ICTIR), 2007.
• 15: Craig Macdonald , Iadh Ounis, Voting techniques for expert search, Knowledge and Information Systems, v.16 n.3, p.259-280, August 2008 [doi>10.1007/s10115-007-0105-3]
• 16: Craig Macdonald , Iadh Ounis, Expertise drift and query expansion in expert search, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, November 06-10, 2007, Lisbon, Portugal [doi>10.1145/1321440.1321490]
• 17: C. Macdonald, D. Hannah, and I. Ounis. High quality expertise evidence for expert search. In Proc. 30th European Conference on Information Retrieval, 2008.
• 18: D. J. C. Mackay and L. Peto. A hierarchical Dirichlet language model. Nat. Language Engin., 1(3):1--19, 1994.
• 19: Desislava Petkova , W. Bruce Croft, Hierarchical Language Models for Expert Finding in Enterprise Corpora, Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence, p.599-608, November 13-15, 2006 [doi>10.1109/ICTAI.2006.63]
• 20: Desislava Petkova , W. Bruce Croft, Proximity-based document representation for named entity retrieval, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, November 06-10, 2007, Lisbon, Portugal [doi>10.1145/1321440.1321542]
• 21: J. Rocchio. Relevance feedback in information retrieval. In The SMART Retrieval System: Experiments in Automatic Document Processing. Prentice Hall, 1971.
• 22: H. Rode, P. Serdyukov, D. Hiemstra, and H. Zaragoza. Entity ranking on graphs: Studies on expert finding. Technical Report TR-CTIT-07-81, University of Twente, 2007.
• 23: P. Serdyukov and D. Hiemstra. Modeling documents as mixtures of persons for expert finding'. In Proc. 30th European Conference on Information Retrieval, pages 309--320, 2008.
• 24: P. Serdyukov, H. Rode, and D. Hiemstra. Modeling relevance propagation for the expert search task. In The Sixteenth Text REtrieval Conference Proceedings (TREC 2007), 2008.
• 25: Chirag Shah , W. Bruce Croft, Evaluating high accuracy retrieval techniques, Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, July 25-29, 2004, Sheffield, United Kingdom [doi>10.1145/1008992.1008996]
• 26: I. Soboroff, A. de Vries, and N. Crawell. Overview of the TREC 2006 Enterprise Track. In The Fifteenth Text REtrieval Conference Proceedings (TREC 2006), 2007.
• 27: Chengxiang Zhai , John Lafferty, A study of smoothing methods for language models applied to Ad Hoc information retrieval, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.334-342, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.384019]
• 28: J. Zhu, D. Song, S. Ruger, M. Eisenstadt, and E. Motta. The Open University at TREC 2006 Enterprise Track Expert Search Task. In The Fourteenth Text REtrieval Conference Proceedings (TREC 2006), 2007.
• 29: J. Zhu, D. Song, and S. Rüger. The Open University at TREC 2007 Enterprise Track. In The Fifteenth Text REtrieval Conference Proceedings (TREC 2007), 2008.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
      H.3.4 Systems and Software
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.2 Types of Systems
      H.4.m Miscellaneous

General Terms:
Algorithms, Experimentation, Measurement, Performance

Keywords:
enterprise search, expert finding, language models, priors, query models