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.
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Notes
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For performance of algorithms other than LR and GB see https://bit.ly/3jp9NA2.
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We also tried other systems: Alchemy, Problog, ProbCog.
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Acknowledgments
This work is supported by the Air Force Office of Scientific Research under award number FA9550-191-0391. SN also acknowledges AFOSR award FA9550-18-1-0462. Any opinions, findings, conclusion or recommendations expressed are those of the authors and do not necessarily reflect the view of AFOSR or the US government. DSD also acknowledges ICT-48 Network of AI Research Excellence Center “TAILOR” (EU Horizon 2020, GA No 952215) and the Collaboration Lab “AI in Construction” (AICO).
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Dhami, D.S., Yan, S., Kunapuli, G., Natarajan, S. (2022). Non-parametric Learning of Embeddings for Relational Data Using Gaifman Locality Theorem. In: Katzouris, N., Artikis, A. (eds) Inductive Logic Programming. ILP 2021. Lecture Notes in Computer Science(), vol 13191. Springer, Cham. https://doi.org/10.1007/978-3-030-97454-1_7
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