KR2022Proceedings of the 19th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning

Haifa, Israel. July 31–August 5, 2022.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-01-0

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Copyright © 2022 International Joint Conferences on Artificial Intelligence Organization

Embed2Sym - Scalable Neuro-Symbolic Reasoning via Clustered Embeddings

  1. Yaniv Aspis(Imperial College London)
  2. Krysia Broda(Imperial College London)
  3. Jorge Lobo(ICREA, Universitat Pompeu Fabra)
  4. Alessandra Russo(Imperial College London)


  1. Neural-symbolic learning
  2. Integrating symbolic and sub-symbolic approaches
  3. Learning symbolic abstractions from unstructured data
  4. Interplay between logic & neural and other learning paradigms


Neuro-symbolic reasoning approaches proposed in recent years combine a neural perception component with a symbolic reasoning component to solve a downstream task. By doing so, these approaches can provide neural networks with symbolic reasoning capabilities, improve their interpretability and enable generalization beyond the training task. However, this often comes at the cost of poor training time, with potential scalability issues. In this paper, we propose a scalable neuro-symbolic approach, called Embed2Sym. We complement a two-stage (perception and reasoning) neural network architecture designed to solve a downstream task end-to-end with a symbolic optimisation method for extracting learned latent concepts. Specifically, the trained perception network generates clusters in embedding space that are identified and labelled using symbolic knowledge and a symbolic solver. With the latent concepts identified, a neuro-symbolic model is constructed by combining the perception network with the symbolic knowledge of the downstream task, resulting in a model that is interpretable and transferable. Our evaluation shows that Embed2Sym outperforms state-of-the-art neuro-symbolic systems on benchmark tasks in terms of training time by several orders of magnitude while providing similar if not better accuracy.