mlsanityData to 14 Oct 2026

Fact or Fiction? Improving Fact Verification with Knowledge Graphs through Simplified Subgraph Retrievals

arXiv 2408.07453 ↗First posted 14 Aug 2024cs.CL cs.AI cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Despite recent success in natural language processing (NLP), fact verification still remains a difficult task. Due to misinformation spreading increasingly fast, attention has been directed towards automatically verifying the correctness of claims. In the domain of NLP, this is usually done by training supervised machine learning models to verify claims by utilizing evidence from trustworthy corpora. We present efficient methods for verifying claims on a dataset where the evidence is in the form of structured knowledge graphs. We use the FactKG dataset, which is constructed from the DBpedia knowledge graph extracted from Wikipedia. By simplifying the evidence retrieval process, from fine-tuned language models to simple logical retrievals, we are able to construct models that both require less computational resources and achieve better test-set accuracy.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 21 of its 40 references that are in the map. First posted on arXiv 14 Aug 2024.

Builds on

21 of 40 references.

References · 21

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2023GPT-4 Technical Reportcited by 18,396
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  5. 2017Graph Attention Networkscited by 8,923
  6. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  7. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  8. 2019TabFact: A Large-scale Dataset for Table-based Fact Verificationcited by 428
  9. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 388
  10. 2021Get Your Vitamin C! Robust Fact Verification with Contrastive Evidencecited by 225
  11. 2019Towards Debiasing Fact Verification Modelscited by 143
  12. 2018Combining Fact Extraction and Verification with Neural Semantic Matching Networkscited by 120
9 more
  1. 2019GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verificationcited by 100
  2. 2018Automated Fact Checking: Task formulations, methods and future directionscited by 96
  3. 2023FactKG: Fact Verification via Reasoning on Knowledge Graphscited by 57
  4. 2018A Retrospective Analysis of the Fake News Challenge Stance Detection Taskcited by 49
  5. 2022GERE: Generative Evidence Retrieval for Fact Verificationcited by 38
  6. 2021FaVIQ: FAct Verification from Information-seeking Questionscited by 36
  7. 2020A Review on Fact Extraction and Verificationcited by 29
  8. 2020DeSePtion: Dual Sequence Prediction and Adversarial Examples for Improved Fact-Checkingcited by 26
  9. 2024FactGenius: Combining Zero-Shot Prompting and Fuzzy Relation Mining to Improve Fact Verification with Knowledge Graphscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  2. 2025Evidence-Grounded Multimodal Misinformation Detection with Attention-Based GNNscited by 2
  3. 2025Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verificationcited by 2
  4. 2026Position: Logical Soundness is not a Reliable Criterion for Neurosymbolic Fact-Checking with LLMscited by 1