Learning Relational Patterns

Michael Geilke, Sandra Zilles

Abstract: Patterns provide a simple, yet powerful means of describing formal languages. However, for many applications, neither patterns nor their generalized versions of typed patterns are expressive enough. This paper extends the model of (typed) patterns by allowing relations between the variables in a pattern. The resulting formal languages are called Relational Pattern Languages (RPLs). We study the problem of learning RPLs from positive data (text) as well as the membership problem for RPLs. These problems are not solvable or not efficiently solvable in general, but we prove positive results for interesting subproblems.