ProVe: A Pipeline for Automated Provenance Verification of Knowledge Graphs against Textual Sources
This addresses the scalability issue of manual provenance verification for Knowledge Graphs, which is crucial for trustworthiness in applications like Wikipedia and search engines, though it appears incremental as it builds on existing methods.
The paper tackles the problem of verifying whether Knowledge Graph triples are supported by their documented provenance, proposing ProVe, an automated pipeline that achieves 87.5% accuracy and 82.9% F1-macro on text-rich sources in a Wikidata dataset.
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.