Relating Relational Learning Algorithms

Relational learning algorithms are of special interest to members of the machine learning community; they ooer practical methods for extending the representations used in algorithms that solve supervised learning tasks. Five approaches are currently being explored to address issues involved with using relational representations. This paper surveys algorithms embodying these approaches, summarizes their empirical evaluations, highlights their commonalities, and suggests potential directions for future research.

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