  In this paper, we conduct an empirical investigation of neural query graph
ranking approaches for the task of complex question answering over knowledge
graphs. We experiment with six different ranking models and propose a novel
self-attention based slot matching model which exploits the inherent structure
of query graphs, our logical form of choice. Our proposed model generally
outperforms the other models on two QA datasets over the DBpedia knowledge
graph, evaluated in different settings. In addition, we show that transfer
learning from the larger of those QA datasets to the smaller dataset yields
substantial improvements, effectively offsetting the general lack of training
data.
