发表机构
Technical University of Cluj-Napoca（克卢日-纳波卡技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种基于知识图谱与对抗多智能体的优先记忆式虚假信息检测框架，在COVID-19虚假信息基准上准确率达97.4%，优于Llama 3.3 70B基线模型。
AI 中文摘要
本文提出了一种混合事实核查框架，该框架将基于知识图谱的语义记忆与对抗性多智能体推理相结合，用于可解释的虚假信息检测。所提系统遵循优先记忆、网页后备的架构，输入的主张首先通过基于Sentence-BERT的语义检索和自然语言推理（NLI）与双索引知识图谱进行评估。当从图谱中检索到的证据不足以支持可靠决策时，该框架会从可信网页来源收集信息，并使用由支持智能体、矛盾智能体和裁决智能体组成的对抗性仲裁庭对其进行评估。一种感知图谱的置信度机制将语义相似度、NLI置信度和图谱结构证据相结合，以确定内部知识是否充足，从而减少不必要的网页检索。验证后，已验证的信息会被转换为结构化三元组并纳入知识图谱，以支持系统语义记忆的增量扩展。在精心整理的COVID-19虚假信息基准上进行的实验评估表明，所提框架在已解决的主张上达到了97.4%的准确率和92.6%的宏平均F1值，优于Llama 3.3 70B基线模型，该基线模型的准确率为87.7%，宏平均F1值为86.3%。
英文摘要
This paper introduces a hybrid fact-checking framework that integrates Knowledge Graph-based semantic memory with adversarial multi-agent reasoning for explainable misinformation detection. The proposed system follows a memory-first, web-fallback architecture, in which input claims are initially evaluated against a dual-index Knowledge Graph through Sentence-BERT-based semantic retrieval and Natural Language Inference. When the evidence retrieved from the graph is insufficient to support a reliable decision, the framework collects information from trusted web sources and assesses it using an adversarial tribunal composed of support, contradiction, and judging agents. A graph-aware confidence mechanism combines semantic similarity, NLI confidence, and structural graph evidence to determine whether internal knowledge is sufficient, thereby reducing unnecessary web retrieval. Following verification, validated information is transformed into structured triples and incorporated into the Knowledge Graph, supporting the incremental expansion of the system's semantic memory. Experimental evaluation on a curated COVID-19 misinformation benchmark demonstrates that the proposed framework achieves an accuracy of 97.4\% and a macro-averaged F1-score of 92.6% on resolved claims, outperforming a Llama~3.3~70B baseline, which obtains an accuracy of 87.7% and a macro-averaged F1-score of 86.3%.
