Tech Reports
Tech Report kmi-00-06 Abstract
Profiling your Customers using Bayesian Networks
Techreport ID: kmi-00-06
Date: 2000
Author(s): Paola Sebastiani, Marco Ramoni and Alexander Crea
This report describes a complete Knowledge Discovery session using Bayesware Discoverer, a program for the induction of Bayesian networks from incomplete data. We build two causal models to help an American Charitable Organization understand the characteristics of respondents to direct mail fund raising campaigns. The first model is a Bayesian network induced from the database of 96,376 Lapsed donors to the June '97 renewal mailing. The network describes the dependency of the probability of response to the renewal mail on a subset of the variables in the database. The second model is a Bayesian network representing the dependency of the dollar amount of the gift on the variables in the same reduced database. This model is induced from the 5% of cases in the database corresponding to the respondents to the renewal campaign. The two models are used for both predicting the expected gift of a donor and understanding the characteristics of donors. These two uses can help the charitable organization to maximize the profit.
Publication(s):
Also in ACM SIGKDD Explorations, 1(2), 2000
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
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Our research in the Semantic Web area looks at the potentials of fusing together advances in a range of disciplines, and applying them in a systemic way to simplify the development of intelligent, knowledge-based web services and to facilitate human access and use of knowledge available on the web. For instance, we are exploring ways in which tnatural language interfaces can be used to facilitate access to data distributed over different repositories. We are also developing infrastructures to support rapid development and deployment of semantic web services, which can be used to create web applications on-the-fly. We are also investigating ways in which semantic technology can support learning on the web, through a combination of knowledge representation support, pedagogical theories and intelligent content aggregation mechanisms. Finally, we are also investigating the Semantic Web itself as a domain of analysis and performing large scale empirical studies to uncover data about the concrete epistemologies which can be found on the Semantic Web. This exciting new area of research gives us concrete insights on the different conceptualizations that are present on the Semantic Web by giving us the possibility to discover which are the most common viewpoints, which viewpoints are mutually inconsistent, to what extent different models agree or disagree, etc...
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