On Lifted Inference Using Neural Embeddings

Authors

  • Mohammad Maminur Islam University of Memphis
  • Somdeb Sarkhel Adobe
  • Deepak Venugopal University of Memphis

DOI:

https://doi.org/10.1609/aaai.v33i01.33017916

Abstract

We present a dense representation for Markov Logic Networks (MLNs) called Obj2Vec that encodes symmetries in the MLN structure. Identifying symmetries is a key challenge for lifted inference algorithms and we leverage advances in neural networks to learn symmetries which are hard to specify using hand-crafted features. Specifically, we learn an embedding for MLN objects that predicts the context of an object, i.e., objects that appear along with it in formulas of the MLN, since common contexts indicate symmetry in the distribution. Importantly, our formulation leverages well-known skip-gram models that allow us to learn the embedding efficiently. Finally, to reduce the size of the ground MLN, we sample objects based on their learned embeddings. We integrate Obj2Vec with several inference algorithms, and show the scalability and accuracy of our approach compared to other state-of-the-art methods.

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Published

2019-07-17

How to Cite

Islam, M. M., Sarkhel, S., & Venugopal, D. (2019). On Lifted Inference Using Neural Embeddings. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 7916-7923. https://doi.org/10.1609/aaai.v33i01.33017916

Issue

Section

AAAI Technical Track: Reasoning under Uncertainty