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.
Abstract
Key personnel turnover creates knowledge gaps in document-based service organizations, where information is distributed across technical specifications, operational databases, and team discussions. 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. Using the Adapter Pattern, the system converts PDF/TXT documents and PostgreSQL tables into a common representation, builds a FAISS vector index, retrieves relevant context, and generates grounded answers with Gemini 2.5 Flash. Evaluation employs eight metrics and three composite scores: Knowledge Transfer Effectiveness (KTE), Multi-Source Retrieval Score (MSRS), and Answer Quality Index (AQI). Experiments were conducted on the BOND_SYS dataset using 25 Indonesian questions covering specification documents, an 8-table PostgreSQL database, and 908 developer discussion messages. Results show perfect retrieval performance (Precision@K = 1.000; MRR = 1.000) across all scenarios. The full hybrid configuration achieves the highest Overall score (0.373), while Scenario C records the highest MSRS (0.825). Scenario E obtains ROUGE-L = 0.181 and BLEU-1 = 0.196 using five manually curated reference answers. Two baseline comparisons further support this contribution: a zero-shot LLM without retrieval correctly answered only 8% of questions, while a BM25 keyword-search baseline, competitive on single-source scenarios, was outperformed on cross-referencing tasks, underscoring the added value of dense multi-source retrieval. The findings demonstrate that integrating formal documents, structured databases, and discussion logs enhances knowledge transfer and question answering for organizational support and employee onboarding.
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.
Hridya Dhulipala, Rajesh Ombase, Michael Wang et al.· 0 citations
Due to the surge in digital document creation, intelligent systems that can effectively retrieve accurate information from unstructured PDF content have become a necessity. Current keyword-based search techniques lack semantic meaning, while large language models (LLMs), when utilized individually, produce hallucinated results when they are not contextually aware of document content. In this paper, we introduce a novel real-time question answering system for PDF content using a lightweight approach to Retrieval-Augmented Generation (RAG). Our system combines a HuggingFace sentence transformer model for semantic embeddings with a FAISS vector similarity search for efficient retrieval, leveraging a Groq-hosted LLaMA 3.3 70B model for context-grounded answer generation. The system is built using LangChain for orchestration and Streamlit for web-based deployment, and requires no GPU, no fine-tuning, and no pre-indexed corpus. Evaluation across five complementary metrics—Exact Match, Token-level F1, BLEU, ROUGE, and semantic similarity—demonstrates strong alignment between generated and reference answers, with a peak semantic similarity of 0.8709 and a mean Token F1 of 0.4701, confirming the viability of this approach for real-world document intelligence.
Mukesh Lakshmi Sai Medikonda, Kalva Vishnu Teja, K. Greeshma et al.· International Conference Com...· 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
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
To address the difficulty faced by university faculty and students in obtaining useful information from massive campus data, this paper proposes an intelligent campus question-and-answer (Q&A) system based on dynamic retrieval-augmented generation (RAG) technology, using campus administrative knowledge as the data source. The system integrates large language models (LLMs) with domain-specific professional knowledge, leveraging the Campus All-in-One project as a foundation. It constructs a campus knowledge base that includes administrative guides, frequently asked questions, and regulatory documents as an external data corpus. By applying the Infinity database, designed specifically for dynamic RAG applications, and employing prompt engineering, the model’s ability to generate accurate and context-aware answers is enhanced. Through this dynamic RAG-based approach tailored for the education domain, the system provides users with interactive access to a wide range of campus administrative information, helping to resolve common issues, simplify inquiry processes for teachers and students, and reduce the workload of campus management.
Charan Thumma, Abhignan Srivatsava Sribhashyam, Chaitanya Tumma et al.· 2026 International Conferenc...· 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.