Supporting Relational Knowledge Discovery:
Lessons in Architecture and Algorithm Design

J. Neville and D. Jensen (2002). Supporting relational knowledge discovery: Lessons in architecture and algorithm design. Proceedings of the Data Mining Lessons Learned Workshop, 19th International Conference on Machine Learning.

Abstract

This paper discusses a few of the lessons we have learned developing a relational knowledge discovery system. The relationships among data instances in relational data provide extra information for “mining.” This additional information has the potential to greatly improve the quality of learned models. However, the dependencies among instances in the data also introduce new statistical challenges for learning algorithms. Relational data provide an ideal environment in which to examine a central challenge of knowledge discovery – its “chicken and egg” character. Data representation can impair the ability to learn important knowledge, but knowing the “right” data representation often requires just that knowledge. With relational data, representation is often a choice; many alternate views of the data provide abundant fodder for reasoning about transformations. In light of this, we discuss representation and design choices that support a co-evolutionary process of knowledge discovery and data transformation in relation data.

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