HetSAGE: Heterogenous Graph Neural Network for Relational Learning (Student Abstract)
Keywords:Neuro-symbolic, Graph Neural Network, Inductive Logic Programming, Inductive Learning, Knowledge Graphs, Relational Learning, Heterogeneous Graphs
AbstractThis paper aims to bridge this gap between neuro-symbolic learning (NSL) and graph neural networks (GNN) approaches and provide a comparative study. We argue that the natural evolution of NSL leads to GNNs, while the logic programming foundations of NSL can bring powerful tools to improve the way information is represented and pre-processed for the GNN. In order to make this comparison, we propose HetSAGE, a GNN architecture that can efficiently deal with the resulting heterogeneous graphs that represent typical NSL learning problems. We show that our approach outperforms the state-of-the-art on 3 NSL tasks: CORA, MUTA188 and MovieLens.
How to Cite
Jankovics, V., Garcia Ortiz, M., & Alonso, E. (2021). HetSAGE: Heterogenous Graph Neural Network for Relational Learning (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15803-15804. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17898
AAAI Student Abstract and Poster Program