# Finding Streams in Knowledge Graphs to Support Fact Checking

arXiv: https://arxiv.org/abs/1708.07239 · HTML: https://mlsanity.com/paper/1708.07239/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2017.

## Built on by (most cited first, 17 of 17)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [ProVe: A Pipeline for Automated Provenance Verification of Knowledge Graphs against Textual Sources](https://mlsanity.com/paper/2210.14846/) (2022)
- [Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking](https://mlsanity.com/paper/2310.07613/) (2023)
- [Knowledge Graph Curation: A Practical Framework](https://mlsanity.com/paper/2208.08130/) (2022)
- [Textual Entailment for Effective Triple Validation in Object Prediction](https://mlsanity.com/paper/2401.16293/) (2024)
- [Validating ChatGPT Facts through RDF Knowledge Graphs and Sentence Similarity](https://mlsanity.com/paper/2311.04524/) (2023)
- [Synergizing Machine Learning & Symbolic Methods: A Survey on Hybrid Approaches to Natural Language Processing](https://mlsanity.com/paper/2401.11972/) (2024)
- [HybridFC: A Hybrid Fact-Checking Approach for Knowledge Graphs](https://mlsanity.com/paper/2409.06692/) (2024)
- [Benchmarking Large Language Models for Knowledge Graph Validation](https://mlsanity.com/paper/2602.10748/) (2026)
- [Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLMs](https://mlsanity.com/paper/2606.08605/) (2026)
- [REMOD: Relation Extraction for Modeling Online Discourse](https://mlsanity.com/paper/2102.11105/) (2021)
- [A Knowledge Enhanced Learning and Semantic Composition Model for Multi-Claim Fact Checking](https://mlsanity.com/paper/2104.13046/) (2021)
- [SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?](https://mlsanity.com/paper/2105.10671/) (2021)
- [Farspredict: A benchmark dataset for link prediction](https://mlsanity.com/paper/2303.14647/) (2023)
- [Survey on Embedding Models for Knowledge Graph and its Applications](https://mlsanity.com/paper/2404.09167/) (2024)
- [Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs](https://mlsanity.com/paper/2511.12817/) (2025)
- [SoK: Formal Methods for Fact-Checking and Information Integrity](https://mlsanity.com/paper/2609.23239/) (2026)

## Abstract (arXiv)

The volume and velocity of information that gets generated online limits current journalistic practices to fact-check claims at the same rate. Computational approaches for fact checking may be the key to help mitigate the risks of massive misinformation spread. Such approaches can be designed to not only be scalable and effective at assessing veracity of dubious claims, but also to boost a human fact checker's productivity by surfacing relevant facts and patterns to aid their analysis. To this end, we present a novel, unsupervised network-flow based approach to determine the truthfulness of a statement of fact expressed in the form of a (subject, predicate, object) triple. We view a knowledge graph of background information about real-world entities as a flow network, and knowledge as a fluid, abstract commodity. We show that computational fact checking of such a triple then amounts to finding a "knowledge stream" that emanates from the subject node and flows toward the object node through paths connecting them. Evaluation on a range of real-world and hand-crafted datasets of facts related to entertainment, business, sports, geography and more reveals that this network-flow model can be very effective in discerning true statements from false ones, outperforming existing algorithms on many test cases. Moreover, the model is expressive in its ability to automatically discover several useful path patterns and surface relevant facts that may help a human fact checker corroborate or refute a claim.