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Yiquan Deng

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Open access Aug 2026

Towards Building a Multi-Source Heterogeneous Knowledge Graph for Complex Material Question Answering

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. · 0 citations
Conference Jul 2026

Design and Experimental Evaluation of A ReAct Agent Framework Integrating Retrieval-Augmented Generation

With the capability enhancement of Large Language Models (LLMs), LLM-based agent systems have demonstrated extraordinary potential in complex task reasoning. However, challenges such as context limitations and knowledge hallucinations persist in practical engineering applications. This paper focuses on the “Design of ReAct Agent Framework Fused with RAG” and proposes a reusable engineering framework. The proposed framework utilizes the ReAct structure as the core for reasoning execution, employs strictly constrained prompts to achieve reliable tool calls (Prompt as Code), integrates Retrieval-Augmented Generation (RAG) as a domain knowledge tool, and enhances system stability through multi-turn context management and error feedback self-correction mechanisms. Based on a general-purpose ReAct project and a specific school policy QA project, this paper establishes a unified experimental setup and conducts main experiments, ablation studies, and case analyses using an author-constructed QA set based on publicly available university policy documents from a specific university. Results on 50 sampled questions show a 100% tool-call success rate for the tool-enabled variants. The ablation results further indicate that context-window management and early stopping reduce the average number of reasoning steps. However, accuracy gains remain inconclusive under the current EM/F1 evaluation and require further validation. This paper provides an engineering pathway reference for designing LLM Agent systems oriented toward real-world business scenarios.

Yiquan Deng · 0 citations