Embedding Symbolic Knowledge into Deep Networks, Yaqi Xie★, Ziwei Xu, Mohan Kankanhalli, Kuldeep S. Meel, and Harold Soh ★, Neural Information Processing Systems (NeurIPS), 2019

In this work, we aim to leverage prior symbolic knowledge to improve the performance of deep models. We propose a graph embedding network that projects propositional formulae (and assignments) onto a manifold via an augmented Graph Convolutional Network (GCN). To generate semantically-faithful embeddings, we develop techniques to recognize node heterogeneity, and semantic regularization that incorporate structural constraints into the embedding. Experiments show that our approach improves the performance of models trained to perform entailment checking and visual relation prediction. Interestingly, we observe a connection between the tractability of the propositional theory representation and the ease of embedding. Future exploration of this connection may elucidate the relationship between knowledge compilation and vector representation learning.

## Resources

You can find our paper here. Check out our repository here on github

## Citation

Please consider citing our paper if you build upon our results and ideas.

Yaqi Xie★, Ziwei Xu, Mohan Kankanhalli, Kuldeep S. Meel, and Harold Soh ★, “Embedding Symbolic Knowledge into Deep Networks”, Neural Information Processing Systems (NeurIPS), 2019

@inproceedings{yaqi2019embedding,
title={Embedding Symbolic Knowledge into Deep Networks},
author={Yaqi, Xie and Xu, Ziwei and Meel, Kuldeep S and Kankanhalli, Mohan and Soh, Harold},
booktitle={Advances in Neural Information Processing Systems},
pages={4235--4245},
year={2019}}