Non-Parametric Learning of Embeddings for Relational Data using Gaifman Locality Theorem

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Abstract

We consider the problem of full model learning from relational data. To this effect, we construct embeddings using symbolic trees learned in a non-parametric manner. The trees are treated as a decision-list of first order rules that are then partially grounded and counted over local neighborhoods of a Gaifman graph to obtain the feature representations. We propose the first method for learning these relational features using a Gaifman graph by using relational tree distances. Our empirical evaluation on real data sets demonstrates the superiority of our approach over handcrafted rules, classical rule-learning approaches, the state-of-the-art relational learning methods and embedding methods.