Non-scalar edge features from knowledge graphs

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!

1 Like

Hi,

I think RGCN in our tutorial can be a starting point for this. Also you can search recent paper about using graph to do recommendation system. Many cases there are trying to solve your questions.