W-RAG is proposed, a source-aware retrieval framework that performs ontology-guided retrieval, local ranking within each knowledge base, and source-level weighting to regulate evidence composition to improve document coverage and generation quality.
Abstract
Retrieval-Augmented Generation (RAG) enables large language models to incorporate external knowledge during generation, improving factual grounding and domain adaptability. However, existing RAG pipelines assume that evidence retrieved from multiple repositories can be ranked globally using a single similarity function. While suitable for open-domain retrieval, this assumption breaks down in enterprise document generation, where heterogeneous knowledge bases (such as policies, regulations, technical documentation, and departmental guidelines) serve distinct roles and must be jointly represented in the generated document. As a result, global ranking often produces unbalanced context dominated by a subset of sources, leading to incomplete enterprise drafts. To address this limitation, we propose W-RAG, a source-aware retrieval framework that performs ontology-guided retrieval, local ranking within each knowledge base, and source-level weighting to regulate evidence composition. We further introduce a new dataset for retrieval-grounded enterprise document generation spanning multiple document types and industry domains. Experiments show that standard RAG pipelines struggle on this task, while W-RAG significantly improves document coverage and generation quality.
This study develops a multi-source Retrieval-Augmented Generation (RAG) based Question Answering (QA) system that automatically integrates heterogeneous knowledge sources through a unified source parameter to enhance knowledge transfer and question answering for organizational support and employee onboarding.
Krisna Dwi Setya Adi, Ivan Michael Siregar· Jurnal Ragam Pengabdian· 0 citations
This work proposes EnSI-RAG (Entity-Structure-Indexed Retrieval-Augmented Generation), a framework that constructs a query-independent, entity-centered index that separates evidence localization from answer synthesis while preserving traceable source evidence.
Xuanyu Meng, Jiashuo Sun, Jash Parekh et al.· 0 citations
Experimental results demonstrate that the VDGR-RAG method significantly outperforms a variety of RAG baselines in terms of both knowledge retrieval recall and QA accuracy.
Wenqi Chen, Haofei Yang, Rui Yang 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
The increasing volume and complexity of institutional documents in public organizations create challenges in accessing reliable knowledge for administrative processes and evidence-based decision-making. Conventional knowledge management systems often rely on keyword-based retrieval, while standalone Large Language Models (LLMs) may generate inaccurate responses when processing domain-specific institutional information. This study proposes a domain-specific Retrieval-Augmented Generation (RAG) framework to enhance institutional knowledge management and AI-assisted decision support in public-sector organizations. The framework was developed using a Design Science Research approach with Universitas Malikussaleh as a case study. The proposed architecture integrates institutional knowledge base construction, semantic retrieval, grounded language generation, and source attribution mechanisms. A knowledge base comprising 416 official institutional documents was developed through document preprocessing, semantic chunking, embedding generation, and vector database indexing. The framework was evaluated using 200 institutional queries based on retrieval performance, response quality, explainability, and system efficiency metrics. The results demonstrate effective retrieval capability, achieving Precision@5 of 0.884, Recall@5 of 0.921, and Mean Reciprocal Rank of 0.895. Generated responses achieved 94.6% factual accuracy, 91.8% contextual relevance, and 96.5% source attribution accuracy, while the hallucination rate was reduced to 3.2%. Furthermore, the framework achieved an average response latency of 1.18 seconds, indicating practical feasibility for institutional applications. These findings demonstrate that integrating semantic retrieval with grounded LLM generation can improve knowledge accessibility, transparency, and reliability for AI-assisted decision support in public organizations. The proposed framework provides a practical foundation for trustworthy institutional knowledge services and supports more efficient, explainable, and evidence-based administrative decision-making across diverse institutional contexts
Yohanes Bowo Widodo· International Journal of Eng...· 0 citations
GraphRAG improves long-document question answering by introducing structured representations beyond conventional retrieval. However, automatically constructed graphs are inherently incomplete projections of source documents, and treating them as independent knowledge sources may lead to unreliable retrieval and generation. We propose PAGE-RAG, a projection-aware adaptive graph retrieval framework for reliable long-document question answering. PAGE-RAG views graph structures as semantic skeletons that organize and navigate document knowledge, rather than replacing the original knowledge source. Based on this perspective, PAGE-RAG introduces a task-adaptive retrieval routing strategy that dynamically selects appropriate retrieval behaviors according to query requirements. Furthermore, PAGE-RAG incorporates strict knowledge boundary control, ensuring that generated responses remain grounded within available evidence and abstaining from unsupported information beyond the accessible knowledge scope. Experiments demonstrate that PAGE-RAG achieves competitive answer quality while improving retrieval efficiency and knowledge reliability, highlighting the importance of projection-aware graph modeling, adaptive retrieval, and explicit knowledge boundary control for trustworthy GraphRAG systems. The source code is publicly available at https://github.com/CXY0112/PAGE-RAG.
Xingyu Chen, Junxiu An, Jun Guo et al.· 0 citations
