Tech Reports
Tech Report kmi-07-02 Abstract
The Open University at TREC 2006 Enterprise Track Expert Search Task
Techreport ID: kmi-07-02
Date: 2007
Author(s): Jianhan Zhu, Dawei Song, Stefan Rüger, Marc Eisenstadt, Enrico Motta
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.
Publication(s):
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
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Multimedia and Information Systems is...

We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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