We thus apply GCNs on the plain version (unlabeled, nondirectional) of schema graphs, ignoring relation types on the edges.
the vector for concept c i ∈ V g in the schema graph g is initialized by their pre-trained embeddings at first (h Then, we update them at the (l + 1)th layer by pooling features of their neighboring nodes (N i ) and their own at the l-th layer with an non-linear activation function σ: In order to capture the relational information in schema graphs, we propose an LSTM-based path encoder on top of the outputs of GCNs.