# Multi-source Knowledge Enhanced Graph Attention Networks for Multimodal Fact Verification

arXiv: https://arxiv.org/abs/2407.10474 · HTML: https://mlsanity.com/paper/2407.10474/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2024.

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

- [DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts](https://mlsanity.com/paper/2412.10510/) (2024)
- [Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models](https://mlsanity.com/paper/2508.03860/) (2025)
- [Contradiction to Consensus: Dual Perspective, Multi Source Retrieval Based Claim Verification with Source Level Disagreement using LLM](https://mlsanity.com/paper/2602.18693/) (2026)
- [MEVER: Multi-Modal and Explainable Claim Verification with Graph-based Evidence Retrieval](https://mlsanity.com/paper/2602.10023/) (2026)
- [D-SECURE: Dual-Source Evidence Combination for Unified Reasoning in Misinformation Detection](https://mlsanity.com/paper/2602.14441/) (2026)

## Abstract (arXiv)

Multimodal fact verification is an under-explored and emerging field that has gained increasing attention in recent years. The goal is to assess the veracity of claims that involve multiple modalities by analyzing the retrieved evidence. The main challenge in this area is to effectively fuse features from different modalities to learn meaningful multimodal representations. To this end, we propose a novel model named Multi-Source Knowledge-enhanced Graph Attention Network (MultiKE-GAT). MultiKE-GAT introduces external multimodal knowledge from different sources and constructs a heterogeneous graph to capture complex cross-modal and cross-source interactions. We exploit a Knowledge-aware Graph Fusion (KGF) module to learn knowledge-enhanced representations for each claim and evidence and eliminate inconsistencies and noises introduced by redundant entities. Experiments on two public benchmark datasets demonstrate that our model outperforms other comparison methods, showing the effectiveness and superiority of the proposed model.