Indiana University-Purdue University Indianapolis (IUPUI) / After the initial network is constructed using expert’s knowledge of the domain,
Bayesian networks need to be updated as and when new data is observed.
Literature mining is a very important source of this new data. In this work, we
explore what kind of data needs to be extracted with the view to update Bayesian Networks, existing technologies which can be useful in achieving some of the goals and what research is required to accomplish the remaining requirements.
This thesis specifically deals with utilizing causal associations and experimental results which can be obtained from literature mining. However, these associations and numerical results cannot be directly integrated with the
Bayesian network. The source of the literature and the perceived quality of
research needs to be factored into the process of integration, just like a human, reading the literature, would. This thesis presents a general methodology for updating a Bayesian Network with the mined data. This methodology consists of solutions to some of the issues surrounding the task of integrating the causal associations with the Bayesian Network and demonstrates the idea with a semiautomated software system.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/2475 |
| Date | 09 March 2011 |
| Creators | Raghuram, Sandeep Mudabail |
| Contributors | Xia, Yuni, Palakal, Mathew, Zou, Xukai, 1963- |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
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