Let’s say we have a knowledge graph where entities are connected by edges of various semantic types (ex: spouseof, synonymof, causedby, etc), and we’d like to perform graph convolution using both node features (some vectorized representation of concept strings) and edge features. What are some ways to represent the edge features? And more generally, what are some approaches to do graph convolution on heterogeneous, multi-relational graphs where the relation types are reasonably complex?
It’s not something I’m working on specifically, but I hope to tackle it in the near future.
I’d appreciate any examples or ideas!