  We introduce DELFT, a factoid question answering system which combines the
nuance and depth of knowledge graph question answering approaches with the
broader coverage of free-text. DELFT builds a free-text knowledge graph from
Wikipedia, with entities as nodes and sentences in which entities co-occur as
edges. For each question, DELFT finds the subgraph linking question entity
nodes to candidates using text sentences as edges, creating a dense and high
coverage semantic graph. A novel graph neural network reasons over the
free-text graph-combining evidence on the nodes via information along edge
sentences-to select a final answer. Experiments on three question answering
datasets show DELFT can answer entity-rich questions better than machine
reading based models, bert-based answer ranking and memory networks. DELFT's
advantage comes from both the high coverage of its free-text knowledge
graph-more than double that of dbpedia relations-and the novel graph neural
network which reasons on the rich but noisy free-text evidence.
