  Knowledge Graph (KG) powered question answering (QA) performs complex
reasoning over language semantics as well as knowledge facts. Graph Neural
Networks (GNNs) learn to aggregate information from the underlying KG, which is
combined with Language Models (LMs) for effective reasoning with the given
question. However, GNN-based methods for QA rely on the graph information of
the candidate answer nodes, which limits their effectiveness in more
challenging settings where critical answer information is not included in the
KG. We propose a simple graph pooling approach that learns useful semantics of
the KG that can aid the LM's reasoning and that its effectiveness is robust
under graph perturbations. Our method, termed SemPool, represents KG facts with
pre-trained LMs, learns to aggregate their semantic information, and fuses it
at different layers of the LM. Our experimental results show that SemPool
outperforms state-of-the-art GNN-based methods by 2.27% accuracy points on
average when answer information is missing from the KG. In addition, SemPool
offers interpretability on what type of graph information is fused at different
LM layers.
