Which popular methods exist to construct input feature vectors X from the semantic information stored in Knowledge Graph?
In my case, the nodes are 2-3 tokens of text; edges are multi-relation. The objective is to bear semantic similarity (such as “play” and “players” are close) rather than any sort of logical reasoning. Then X would be used to feed R-GNN.
alice [friend] bob (football players) [play] (in a team) bob [play] football
My initial thought is Gensim (and therefore the problem of constructing input feature vectors is out of the scope of GNN), but maybe some other tools/methods exist or provided by DGL or pytorch geometric?