# ProVe: A Pipeline for Automated Provenance Verification of Knowledge Graphs against Textual Sources

arXiv: https://arxiv.org/abs/2210.14846 · HTML: https://mlsanity.com/paper/2210.14846/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 74 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 30 of 30)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Explainability in Graph Neural Networks: A Taxonomic Survey](https://mlsanity.com/paper/2012.15445/) (2020)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Get Your Vitamin C! Robust Fact Verification with Contrastive Evidence](https://mlsanity.com/paper/2103.08541/) (2021)
- [Investigating Pretrained Language Models for Graph-to-Text Generation](https://mlsanity.com/paper/2007.08426/) (2020)
- [The Fact Extraction and VERification (FEVER) Shared Task](https://mlsanity.com/paper/1811.10971/) (2018)
- [Fine-grained Fact Verification with Kernel Graph Attention Network](https://mlsanity.com/paper/1910.09796/) (2019)
- [GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification](https://mlsanity.com/paper/1908.01843/) (2019)
- [Automated Fact Checking: Task formulations, methods and future directions](https://mlsanity.com/paper/1806.07687/) (2018)
- [Reasoning Over Semantic-Level Graph for Fact Checking](https://mlsanity.com/paper/1909.03745/) (2019)
- [UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification](https://mlsanity.com/paper/1809.01479/) (2018)
- [BERT for Evidence Retrieval and Claim Verification](https://mlsanity.com/paper/1910.02655/) (2019)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News](https://mlsanity.com/paper/2010.03159/) (2020)
- [A Study of the Quality of Wikidata](https://mlsanity.com/paper/2107.00156/) (2021)
- [Discriminative Predicate Path Mining for Fact Checking in Knowledge Graphs](https://mlsanity.com/paper/1510.05911/) (2015)
- [Finding Streams in Knowledge Graphs to Support Fact Checking](https://mlsanity.com/paper/1708.07239/) (2017)
- [Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques and Assurance Actions](https://mlsanity.com/paper/1801.02546/) (2018)
- [Team Papelo: Transformer Networks at FEVER](https://mlsanity.com/paper/1901.02534/) (2019)
- [Boilerplate Removal using a Neural Sequence Labeling Model](https://mlsanity.com/paper/2004.14294/) (2020)
- [Web2Text: Deep Structured Boilerplate Removal](https://mlsanity.com/paper/1801.02607/) (2018)
- [Assessing the quality of sources in Wikidata across languages: a hybrid approach](https://mlsanity.com/paper/2109.09405/) (2021)
- [WDV: A Broad Data Verbalisation Dataset Built from Wikidata](https://mlsanity.com/paper/2205.02627/) (2022)
- [Visualizing and Explaining Language Models](https://mlsanity.com/paper/2205.10238/) (2022)

## Built on by (most cited first, 5 of 5)

- [From Documents to Dialogue: Building KG-RAG Enhanced AI Assistants](https://mlsanity.com/paper/2502.15237/) (2025)
- [Evaluation of Pipelines for Data Integration into Knowledge Graphs](https://mlsanity.com/paper/2605.22304/) (2026)
- [Evaluating Class Membership Relations in Knowledge Graphs using Large Language Models](https://mlsanity.com/paper/2404.17000/) (2024)
- [Schema-Aware Planning and Hybrid Knowledge Toolset for Reliable Knowledge Graph Triple Verification](https://mlsanity.com/paper/2604.04190/) (2026)
- [Validating FKG.in: Soundness Assessment in LLM-Augmented Indian Food Knowledge](https://mlsanity.com/paper/2608.29249/) (2026)

## Abstract (arXiv)

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.