  Knowledge Graphs are repositories of information that gather data from a
multitude of domains and sources in the form of semantic triples, serving as a
source of structured data for various crucial applications in the modern web
landscape, from Wikipedia infoboxes to search engines. Such graphs mainly serve
as secondary sources of information and depend on well-documented and
verifiable provenance to ensure their trustworthiness and usability. However,
their ability to systematically assess and assure the quality of this
provenance, most crucially whether it properly supports the graph's
information, relies mainly on manual processes that do not scale with size.
ProVe aims at remedying this, consisting of a pipelined approach that
automatically verifies whether a Knowledge Graph triple is supported by text
extracted from its documented provenance. ProVe is intended to assist
information curators and consists of four main steps involving rule-based
methods and machine learning models: text extraction, triple verbalisation,
sentence selection, and claim verification. ProVe is evaluated on a Wikidata
dataset, achieving promising results overall and excellent performance on the
binary classification task of detecting support from provenance, with 87.5%
accuracy and 82.9% F1-macro on text-rich sources. The evaluation data and
scripts used in this paper are available on GitHub and Figshare.
