  To retrieve more relevant, appropriate and useful documents given a query,
finding clues about that query through the text is crucial. Recent deep
learning models regard the task as a term-level matching problem, which seeks
exact or similar query patterns in the document. However, we argue that they
are inherently based on local interactions and do not generalise to ubiquitous,
non-consecutive contextual relationships. In this work, we propose a novel
relevance matching model based on graph neural networks to leverage the
document-level word relationships for ad-hoc retrieval. In addition to the
local interactions, we explicitly incorporate all contexts of a term through
the graph-of-word text format. Matching patterns can be revealed accordingly to
provide a more accurate relevance score. Our approach significantly outperforms
strong baselines on two ad-hoc benchmarks. We also experimentally compare our
model with BERT and show our advantages on long documents.
