# Discriminative Predicate Path Mining for Fact Checking in Knowledge Graphs

arXiv: https://arxiv.org/abs/1510.05911 · HTML: https://mlsanity.com/paper/1510.05911/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2015.

## Built on by (most cited first, 23 of 23)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection](https://mlsanity.com/paper/2101.11206/) (2021)
- [KGARevion: An AI Agent for Knowledge-Intensive Biomedical QA](https://mlsanity.com/paper/2410.04660/) (2024)
- [Cheap-fake Detection with LLM using Prompt Engineering](https://mlsanity.com/paper/2306.02776/) (2023)
- [Logical Consistency of Large Language Models in Fact-checking](https://mlsanity.com/paper/2412.16100/) (2024)
- [Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection](https://mlsanity.com/paper/2306.02137/) (2023)
- [Contrastive Knowledge Graph Error Detection](https://mlsanity.com/paper/2211.10030/) (2022)
- [ProVe: A Pipeline for Automated Provenance Verification of Knowledge Graphs against Textual Sources](https://mlsanity.com/paper/2210.14846/) (2022)
- [Harnessing Diverse Perspectives: A Multi-Agent Framework for Enhanced Error Detection in Knowledge Graphs](https://mlsanity.com/paper/2501.15791/) (2025)
- [Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey](https://mlsanity.com/paper/2201.08032/) (2022)
- [MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection](https://mlsanity.com/paper/2210.05401/) (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)
- [Neural models for Factual Inconsistency Classification with Explanations](https://mlsanity.com/paper/2306.08872/) (2023)
- [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)
- [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)
- [Multilingual Disinformation Detection for Digital Advertising](https://mlsanity.com/paper/2207.10649/) (2022)
- [Assessing Automated Fact-Checking for Medical LLM Responses with Knowledge Graphs](https://mlsanity.com/paper/2511.12817/) (2025)
- [Spectral Analysis of Fake News Propagation](https://mlsanity.com/paper/2605.13861/) (2026)
- [SoK: Formal Methods for Fact-Checking and Information Integrity](https://mlsanity.com/paper/2609.23239/) (2026)

## Abstract (arXiv)

Traditional fact checking by experts and analysts cannot keep pace with the volume of newly created information. It is important and necessary, therefore, to enhance our ability to computationally determine whether some statement of fact is true or false. We view this problem as a link-prediction task in a knowledge graph, and present a discriminative path-based method for fact checking in knowledge graphs that incorporates connectivity, type information, and predicate interactions. Given a statement S of the form (subject, predicate, object), for example, (Chicago, capitalOf, Illinois), our approach mines discriminative paths that alternatively define the generalized statement (U.S. city, predicate, U.S. state) and uses the mined rules to evaluate the veracity of statement S. We evaluate our approach by examining thousands of claims related to history, geography, biology, and politics using a public, million node knowledge graph extracted from Wikipedia and PubMedDB. Not only does our approach significantly outperform related models, we also find that the discriminative predicate path model is easily interpretable and provides sensible reasons for the final determination.