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The Cubic Regression Model for Merging Results from Multiple Text Databases

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Wu, Shengli, Bi, Yaxin and Liu, Jun (2009) The Cubic Regression Model for Merging Results from Multiple Text Databases. In: Semantics, Knowledge and Grid, 2009. SKG 2009. Fifth International Conference on, Zhuhai, China. IEEE xplore. 8 pp. [Conference contribution]

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URL: http://www.ieeexplore.ieee.org/search/srchabstract.jsp?arnumber=5368587&isnumber=5368022&punumber=5368021&k2dockey=5368587@ieeecnfs&query=((shengli+wu)%3Cin%3Emetadata)&pos=1&access=no

DOI: 10.1109/SKG.2009.100

Abstract

In a distributed information retrieval system, how to merge results from different text databases is an important issue, since it affects the effectiveness of the result considerably. In many cases, the underlining systems only provide a ranked list of documents for any information need. In this paper, we investigate the relation between rank and relevance in resultant document lists, and find that the cubic model is a good option for this. Extensive experimentation is conducted to evaluate the performance of the cubic model for results merging. The experimental results demonstrate that the cubic model is better than the logistic model, which was suggested by a previous research.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:11762
Deposited By:Dr Shengli Wu
Deposited On:12 Feb 2010 11:37
Last Modified:12 Feb 2010 11:37

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