RA-QGQA: A Question-Driven Pipeline for Corpus-Grounded Knowledge Graph Verification
LLM-driven construction can populate a knowledge graph (KG) with large volumes of triples extracted from documents, yet hallucination leaves their quality far from guaranteed. Existing verification methods return only a single label, one that fails to indicate which part of the triple is at fault and offers no evidence to check against. We present RA-QGQA, which recasts triple verification as a question-driven, corpus-grounded task. For each triple, a Question Generation (QG) module poses one question per slot (head, relation, and tail); a Retrieval-Augmented Generation (RAG) Question Answering (QA) module retrieves from the corpus and answers each independently; the answers are then matched against the triple to reach a verdict. The verdict pinpoints the faulty slot and carries a passage-level evidence chain the user can trace back to the source. We demonstrate RA-QGQA as an interactive web system in which users import a KG and its source corpus, verify all triples in a single pass, and, for any flagged triple, inspect the passages that justify its verdict. The system thus turns large-scale KG verification into a scalable, evidence-backed process.