  Commonsense reasoning aims to empower machines with the human ability to make
presumptions about ordinary situations in our daily life. In this paper, we
propose a textual inference framework for answering commonsense questions,
which effectively utilizes external, structured commonsense knowledge graphs to
perform explainable inferences. The framework first grounds a question-answer
pair from the semantic space to the knowledge-based symbolic space as a schema
graph, a related sub-graph of external knowledge graphs. It represents schema
graphs with a novel knowledge-aware graph network module named KagNet, and
finally scores answers with graph representations. Our model is based on graph
convolutional networks and LSTMs, with a hierarchical path-based attention
mechanism. The intermediate attention scores make it transparent and
interpretable, which thus produce trustworthy inferences. Using ConceptNet as
the only external resource for Bert-based models, we achieved state-of-the-art
performance on the CommonsenseQA, a large-scale dataset for commonsense
reasoning.
