Hybrid AI Fact-Checking: Knowledge Graphs + LLMs + Search, Explained

Kari Jaaskelainen

06 Nov 2025 — 1 min read

Smarter, more transparent fact-checking

Large language models sound confident, but not always correct. Knowledge graphs are precise but can miss facts. This study combines both—plus a selective web-search agent—into one interpretable fact-checking pipeline.

Step 1: Rapid one-hop lookups in DBpedia to grab trusted facts.
Step 2: An LLM assigns a rule-guided label (Supported or Refuted) and explains why.
Step 3: If coverage is missing, a search agent fetches up-to-date sources.

On the FEVER benchmark (Supported/Refuted), it reached an F1 score of 0.93—without task-specific fine-tuning.

For claims labeled “Not Enough Information,” a targeted reannotation showed the system often surfaced valid evidence missed before, confirmed by expert annotators and LLM reviewers.

Bottom line: Hybrid tools can be accurate and explainable, with fallbacks that curb hallucinations and improve coverage. The authors release a modular, open-source system that generalizes across datasets.

Paper: http://arxiv.org/abs/2511.03217v1

Paper: http://arxiv.org/abs/2511.03217v1

Register: https://www.AiFeta.com

#FactChecking #AI #LLM #KnowledgeGraph #NLP #Misinformation #Search #FEVER #OpenSource #ExplainableAI

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