  Verifying the veracity of claims requires reasoning over a large knowledge
base, often in the form of corpora of trustworthy sources. A common approach
consists in retrieving short portions of relevant text from the reference
documents and giving them as input to a natural language inference module that
determines whether the claim can be inferred or contradicted from them. This
approach, however, struggles when multiple pieces of evidence need to be
collected and combined from different documents, since the single documents are
often barely related to the target claim and hence they are left out by the
retrieval module. We conjecture that a graph-based approach can be beneficial
to identify fragmented evidence. We tested this hypothesis by building, over
the whole corpus, a large graph that interconnects text portions by means of
mentioned entities and exploiting such a graph for identifying candidate sets
of evidence from multiple sources. Our experiments show that leveraging on a
graph structure is beneficial in identifying a reasonably small portion of
passages related to a claim.
