Non-parametric learning of embeddings for relational data using Gaifman locality theorem. (English) Zbl 1524.68270
Katzouris, Nikos (ed.) et al., Inductive logic programming. 30th international conference, ILP 2021, virtual event, October 25–27, 2021. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 13191, 95-110 (2022).
Summary: 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.
For the entire collection see [Zbl 1516.68017].
For the entire collection see [Zbl 1516.68017].
MSC:
| 68T05 | Learning and adaptive systems in artificial intelligence |
Keywords:
statistical relational learning; Gaifman locality theorem; embeddings; relational density estimationReferences:
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