Aug 2026· Proceedings of the 2026 ACM Symposium on Document Engineering· 0 citations· 7 references
TL;DR
RA-QGQA is presented, which recasts triple verification as a question-driven, corpus-grounded task, and demonstrates 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 inspect the passages that justify its verdict.
Abstract
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.
Question-answer (QA) pairs are widely used in knowledge base construction, question-answering systems, and the post-training of large language models (LLMs). However, important knowledge in long documents is often distributed across multiple paragraphs and connected through complex entity relationships. Such fragmented and relational knowledge poses substantial challenges for existing QA generation methods, which often fail to adequately cover core document content, cross-paragraph semantic connections, and multi-entity relationships. We present GraphQAG, a knowledge graph-guided visual analytics framework for generating high-quality QA pairs from long documents. GraphQAG follows a three-stage workflow. First, it constructs a document knowledge graph by segmenting the document into paragraphs and extracting salient entities and relations. Second, it builds a graph-based generation space from entities, relations, and multi-hop paths to constrain and guide LLM-based QA generation. Third, it uses the knowledge graph as an interactive visual representation, enabling users to explore document knowledge structures, inspect the coverage and evidence provenance of generated QA pairs, and iteratively refine the QA pair set through graph-based interactions. We evaluated GraphQAG through a user study with 16 participants, two case studies, and expert interviews. The results indicate that GraphQAG effectively supports users in identifying knowledge coverage gaps, examining generated QA pairs, and refining the QA pair set. These findings demonstrate the usefulness of combining knowledge graphs, LLM-based generation, and visual analytics for producing more comprehensive and trustworthy QA pairs from long documents.
KGCaRe is proposed, a hybrid approach that combines neural retrieval with symbolic reasoning over LLM-generated KGs that consistently outperforms existing baselines, including Vanilla LLM, Code Prompt, Text Prompt, Think-on-Graph, Vanilla RAG, and HybridContextQA.
Ghanshyam Verma, Sima Sarkar, Devishree Pillai et al.· 0 citations
Large Language Models (LLMs) show considerable potential for materials-science question answering. However, LLM responses may still be affected by unsupported parametric associations, while dense Retrieval-Augmented Generation (RAG) can fragment relational evidence across text chunks. Moreover, general graph-based retrieval does not necessarily preserve the hierarchical relations and factual attributes required to resolve implicit material constraints. To address these limitations, we propose MCTD-KG, a multi-source heterogeneous knowledge graph integrated with a Knowledge-Enhanced RAG framework for complex material question answering. MCTD-KG adopts a Classification–Term–Data ontology to connect disciplinary taxonomies, domain concepts, semantic relations, and empirical records from toolbooks and the scientific literature. Through LLM-assisted knowledge extraction, entity normalization, and multi-source integration, the resulting graph contains more than 530,000 entities across three layers, including 61,768 text-extracted Term-layer entities. During inference, Dual-Channel Retrieval jointly retrieves query-relevant relational paths and associated material attributes, while an explicit semantic filtering stage screens candidate evidence against the query constraints. Evaluation on an expert-validated benchmark of 1577 questions shows that the proposed framework achieves an overall accuracy of 68.48%, compared with 17.40% for the zero-shot Pure LLM, 24.79% for the best Vanilla RAG setting, and 44.96% for GraphRAG. It also achieves 45.22% accuracy on four-hop questions, compared with 39.49% for GraphRAG. These results indicate that integrating multi-source domain knowledge with relation-preserved retrieval and attribute-supported filtering provides more focused and inspectable evidence, thereby supporting more accurate complex material question answering.
Peize Li, Xi Guo, Nan Yin et al.· Electronics· 0 citations
By evaluating paper retrieval, evidence grounding, and answer accuracy separately, LitTraceQA provides a testbed for scientific QA systems that produce verifiable answers rather than unsupported summaries.
Retrieval-augmented generation (RAG) systems commonly operate on text extracted from documents, potentially losing information contained in figures, tables, layout, and relationships distributed across passages. We present an explainable multimodal graph-RAG architecture that augments a text-only baseline with LLM-extracted subject--relation--object triples and CLIP-based retrieval of figures and tables. The three evidence sources are retrieved independently and fused only at generation time, allowing the effects of graph evidence, visual evidence, and generator choice to be evaluated separately. We conduct a controlled four-way ablation on 1,000 PubLayNet pages across single-passage, multi-hop, and figure questions using two closed-weight and two open-weight multimodal generators. We also compare matched caption-answerable and pixel-only figure question sets to distinguish caption recovery from genuine visual question answering. Knowledge-graph augmentation provides no reliable accuracy improvement across generators or question types in this corpus. On pixel-only questions, text-only systems achieve zero accuracy, while multimodal systems reach 0.057--0.114 and are constrained by both image retrieval (Recall@3 = 0.371) and the generators'ability to interpret dense scientific figures and tables. Caption-derived questions substantially overestimate text-only visual question-answering capability. Processing the same image also produces an elevenfold variation in input tokens across generators, showing that image tokenisation can dominate deployment cost. Source code, experiment configurations, and evaluation resources are publicly available at https://github.com/sokistar24/multimodal-graph-rag. These findings show that the value of multimodal and graph augmentation depends on retrieval design, corpus structure, benchmark construction, and generator efficiency.