I have been a few months into PyNeuraLogic because of its focus on Relational Deep Learning (in particular Lifted Relational Neural Networks). I would say the main selling point is extending GNNs (Graph Neural Networks)
From what I can see on the Scallop website, the focus is on vision and NLP.
So both are similar approaches combining deep learning with symbolic reasoning (and both are based in Datalog) but the problems they are tackling are quite different. Also, both approaches have made it to top conferences like NeurIPS and ICLR, so I guess this field is gaining momentum.
Just based on the docs, on the lowest level they both create a mapping from input features to relations. Then NeuraLogic has an example for implementing GCN like message passing with a simple relational rule:
```
node2(X) <= W node1(Y), edge(X, Y)
```
Do you know, is it possible to implement something on Scallop as well, or are the differences much larger?
The GCN implementation is based on the concept of templating (or lifting) which is not exploited in Scallop. In fact, this is the key idea of Neuralogic for joining deep learning and relational-logic representations.
From what I can see on the Scallop website, the focus is on vision and NLP.
So both are similar approaches combining deep learning with symbolic reasoning (and both are based in Datalog) but the problems they are tackling are quite different. Also, both approaches have made it to top conferences like NeurIPS and ICLR, so I guess this field is gaining momentum.