# Incorporating Symbolic Domain Knowledge into Graph Neural Networks

arXiv: https://arxiv.org/abs/2010.13900 · HTML: https://mlsanity.com/paper/2010.13900/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [Incorporating Domain Knowledge into Deep Neural Networks](https://mlsanity.com/paper/2103.00180/) (2021)
- [On the Challenges and Opportunities in Generative AI](https://mlsanity.com/paper/2403.00025/) (2024)
- [Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment](https://mlsanity.com/paper/2105.10709/) (2021)
- [Survey of Generative Methods for Social Media Analysis](https://mlsanity.com/paper/2112.07041/) (2021)
- [Hypergraph Convolutional Networks for Fine-grained ICU Patient Similarity Analysis and Risk Prediction](https://mlsanity.com/paper/2308.12575/) (2023)
- [Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification](https://mlsanity.com/paper/2105.09448/) (2021)
- [A Review of Some Techniques for Inclusion of Domain-Knowledge into Deep Neural Networks](https://mlsanity.com/paper/2107.10295/) (2021)
- [Incorporation of Deep Neural Network & Reinforcement Learning with Domain Knowledge](https://mlsanity.com/paper/2107.14613/) (2021)
- [Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs](https://mlsanity.com/paper/2205.06783/) (2022)
- [Composition of Relational Features with an Application to Explaining Black-Box Predictors](https://mlsanity.com/paper/2206.00738/) (2022)
- [Neural Feature-Adaptation for Symbolic Predictions Using Pre-Training and Semantic Loss](https://mlsanity.com/paper/2211.16047/) (2022)
- [FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification](https://mlsanity.com/paper/2509.10510/) (2025)

## Abstract (arXiv)

Our interest is in scientific problems with the following characteristics: (1) Data are naturally represented as graphs; (2) The amount of data available is typically small; and (3) There is significant domain-knowledge, usually expressed in some symbolic form. These kinds of problems have been addressed effectively in the past by Inductive Logic Programming (ILP), by virtue of 2 important characteristics: (a) The use of a representation language that easily captures the relation encoded in graph-structured data, and (b) The inclusion of prior information encoded as domain-specific relations, that can alleviate problems of data scarcity, and construct new relations. Recent advances have seen the emergence of deep neural networks specifically developed for graph-structured data (Graph-based Neural Networks, or GNNs). While GNNs have been shown to be able to handle graph-structured data, less has been done to investigate the inclusion of domain-knowledge. Here we investigate this aspect of GNNs empirically by employing an operation we term "vertex-enrichment" and denote the corresponding GNNs as "VEGNNs". Using over 70 real-world datasets and substantial amounts of symbolic domain-knowledge, we examine the result of vertex-enrichment across 5 different variants of GNNs. Our results provide support for the following: (a) Inclusion of domain-knowledge by vertex-enrichment can significantly improve the performance of a GNN. That is, the performance VEGNNs is significantly better than GNNs across all GNN variants; (b) The inclusion of domain-specific relations constructed using ILP improves the performance of VEGNNs, across all GNN variants. Taken together, the results provide evidence that it is possible to incorporate symbolic domain knowledge into a GNN, and that ILP can play an important role in providing high-level relationships that are not easily discovered by a GNN.