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The Open University at TREC 2006 Enterprise Track Expert Search Task

The Multimedia and Information Systems group at the Knowledge Media Institute of the Open University par-ticipated in the Expert Search task of the Enterprise Track in TREC 2006. We have proposed to address three main innovative points in a two-stage language model, which consists of a document relevance model and a co-occurrence model, in order to improve the performance of expert search. The three innovative points are based on characteristics of documents. First, document authority in terms of their PageRanks is considered in the document relevance model. Second, document internal structure is taken into account in the co-occurrence model. Third, we consider multiple levels of associations between experts and query terms in the co-occurrence model. Our experi-ments on the TREC2006 Expert Search task show that addressing the above three points has led to improved effectiveness of expert search on the W3C dataset.


Zhu, J., Song, D., Rüger, S., Eisenstadt, M. and Motta, E. (2006) The Open University at TREC 2006 Enterprise Track Expert Search Task. In Proc. of The Fifteenth Text REtrieval Conference (TREC 2006), Gaithersburg, Maryland USA, National Institute of Standards and Technology, USA

ID: kmi-07-02

Date: 2007

Author(s): Jianhan Zhu, Dawei Song, Stefan Rüger, Marc Eisenstadt, Enrico Motta

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Marie Curie Early Stage Researcher

Knowledge Media Institute (KMi)
£45,463 per year plus allowances
Based in Milton Keynes
Full-time, Fixed-term contract for 36 months

The Knowledge Media Institute invites applications for an Early Stage Researcher to work in the area of bias in Artificial Intelligence. The researcher will work within the interdisciplinary community of the NoBias project and will pursue a PhD in Computer Science at the Open University as part of their 3-year engagement with the project. NoBIAS Artificial Intelligence without Bias, is a project funded by the European Union's Horizon 2020 research and innovation programme under the Marie...


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