Dynamic Knowledge Graph Augmentation Enhances Factual Accuracy in Retrieval Based Generation

Rafael Costa; Elisabeth Gruber

doi:10.54097/e4991d70

Authors

Rafael Costa
Elisabeth Gruber

DOI:

https://doi.org/10.54097/e4991d70

Keywords:

Retrieval-augmented generation, Dynamic knowledge graph, Knowledge graph augmentation, Factual accuracy, Large language models, Graph neural networks, Hallucination reduction

Abstract

Large language models (LLMs) demonstrate exceptional fluency in natural language generation but remain susceptible to producing factually incorrect outputs due to static parametric knowledge frozen at training time. Retrieval-augmented generation (RAG) partially mitigates this limitation by conditioning generation on externally retrieved evidence, yet conventional RAG systems depend on unstructured, flat document corpora that fail to represent the relational and temporal dynamics characterizing real-world knowledge. This paper proposes Dynamic Knowledge Graph Augmentation (DKGA), a framework that integrates continuously updated knowledge graphs (KGs) with retrieval-based generation pipelines to substantially improve factual accuracy. DKGA employs a graph neural network (GNN) encoder for subgraph-conditioned entity representation learning, a temporal update module for incremental knowledge refresh, and a cross-modal relevance-aware fusion mechanism that jointly conditions the generator on structured KG evidence and unstructured text passages. Experiments on the WebQA and TriviaQA benchmarks demonstrate that DKGA achieves an 11.4% improvement in exact match factual accuracy and an 18.2% reduction in hallucination rate over strong RAG baselines. These results provide compelling evidence that dynamic, structured knowledge representations are a critical and underutilized resource for knowledge-intensive language generation.

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References

[1] Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., ... & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in neural information processing systems, 33, 9459-9474.

[2] Guu, K., Lee, K., Tung, Z., Pasupat, P., & Chang, M. (2020, November). Retrieval augmented language model pre-training. In International conference on machine learning (pp. 3929-3938). PMLR.

[3] Chen, T., & Ding, J. (2026). Cold Start Latency Optimization Strategies for Function as a Service Platforms. Computer Life, 14(1), 64-73.

[4] Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., ... & Yih, W. T. (2020, November). Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP) (pp. 6769-6781).

[5] Izacard, G., & Grave, E. (2021, April). Leveraging passage retrieval with generative models for open domain question answering. In Proceedings of the 16th conference of the european chapter of the association for computational linguistics: main volume (pp. 874-880).

[6] Wu, S., Tang, J., Yang, B., Wang, A., Jia, K., Yu, J., ... & Su, J. (2024). Not all languages are equal: Insights into multilingual retrieval-augmented generation. arXiv preprint arXiv:2410.21970.

[7] Wang, Z., Shen, Z., Wang, B., & Shang, W. (2025). Modernizing Enterprise Analytics through Low-Code Automation and Cloud-Native Data Architectures. Asian Business Research Journal, 10(12), 20-33.

[8] Zhao, X., Sun, T., Ren, S., Yang, J., & Liu, Y. (2025). RAG-Based AI Agents for Enterprise Software Development: Implementation Patterns and Production Deployment. Frontiers in Artificial Intelligence Research, 2(3), 501-520.

[9] Li, P., Liu, J., & Qiu, L. (2026). Deep Learning Methods for Demand Forecasting and Inventory Optimization in Modern Supply Chains. Asian Business Research Journal, 11(3), 21-29.

[10] Qiu, L. (2025). Reinforcement Learning Approaches for Intelligent Control of Smart Building Energy Systems with Real-Time Adaptation to Occupant Behavior and Weather Conditions. Journal of Computing and Electronic Information Management, 18(2), 32-37.

[11] Zhang, H. (2025). Reinforcement Learning Approaches for Layout Optimization in Electronic Design Automation with Electromagnetic Compatibility Constraints. Frontiers in Robotics and Automation, 2(2), 77-93.

[12] Shen, Z., Zhao, W., Wang, B., Wang, Z., & Shang, W. (2026). CAGR: A Cross-Accelerator Graph Optimization Framework for Efficient Recommender System Inference. IEEE Access.

[13] Sun, T., Wang, M., & Han, X. (2025). Deep Learning in Insurance Fraud Detection: Techniques, Datasets, and Emerging Trends. Journal of Banking and Financial Dynamics, 9(8), 1-11.

[14] Liu, J., Li, P., & Wang, Y. (2026). Graph Neural Networks for Modeling Complex Dependencies in Global Supply Chain Networks. Journal of Computing and Electronic Information Management, 20(3), 9-20.

[15] Zhang, F., & Wu, B. (2025). Large Language Models as General Purpose Intelligence Systems for Reasoning, Planning and Decision Making. American Journal of Artificial Intelligence and Neural Networks, 6(4), 45-72.

[16] Li, P., Ren, S., Zhang, Q., Wang, X., & Liu, Y. (2024). Think4SCND: Reinforcement learning with thinking model for dynamic supply chain network design. IEEE Access, 12, 195974-195985.

[17] Zhang, F., & Yang, J. S. (2025). Learning Driven Decision Intelligence for Autonomous Driving Through Multimodal Understanding World Modeling and Policy Optimization. Frontiers in Artificial Intelligence Research, 2(3), 616-634.

[18] Wang, B., Wang, Z., Zhao, W., & Liu, Y. (2025). Network Fabric Simulation and Validation for Data Center Routing Convergence Under Large-Scale Failure Scenarios. Computer Science Bulletin, 8(01), 310-326.

[19] Liu, J., Wang, J., Chen, H., Guinness, J., Martin, R., & Kulkarni, C. S. (2019). Optimal Level Crossing Predictions for Electronic Prognostics. In AIAA Scitech 2019 Forum (p. 1962).

[20] Chen, J., Cui, Y., Zhang, X., Yang, J., & Zhou, M. (2024). Temporal convolutional network for carbon tax projection: A data-driven approach. Applied Sciences, 14(20), 9213.

[21] Wei, Z., Sun, T., & Zhou, M. (2024). LIRL: Latent Imagination-Based Reinforcement Learning for Efficient Coverage Path Planning. Symmetry, 16(11), 1537.

[22] Zhang, S., Qiu, L., & Zeng, Z. (2026). Physics-Data Synergy in Structural Health Monitoring: A Multi-Scale Graph Contrastive Framework With Temperature-Adaptive Fusion. IEEE Access.

[23] Zeng, Z., Lin, H., Zhang, S., & Wang, B. (2026). Adaptive Robust Watermarking for Large Language Models via Dynamic Token Embedding Perturbation. IEEE Access, 14, 9319-9339.

[24] Qiu, L. (2025). Multi-Agent Reinforcement Learning for Coordinated Smart Grid and Building Energy Management Across Urban Communities. Computer Life, 13(3), 8-15.

[25] Zhao, W., Chen, T., Yang, J. S., & Qiu, L. (2026). AutoML-Pipeline: A RAG-enhanced code generation framework with pre-validation for cloud-native machine learning workflows. IEEE Access.

[26] Yang, Y., & Yang, J. (2026). Synthetic Data Meets Finance: Generative Models for Privacy Preserving Analytics. Journal of Banking and Financial Dynamics, 10(4), 1-8.

[27] Yang, F., Zhang, H., Tao, S., & Hao, S. (2022). Graph representation learning via simple jumping knowledge networks. Applied Intelligence, 52(10), 11324-11342.

[28] Mavi, V., Jangra, A., & Jatowt, A. (2024). Multi-hop question answering. Foundations and Trends® in Information Retrieval, 17(4), 457-586.

[29] Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019). Graph convolutional networks: a comprehensive review. Computational Social Networks, 6(1), 1-23.

[30] Zhang, H., Lu, G., Zhan, M., & Zhang, B. (2022). Semi-supervised classification of graph convolutional networks with Laplacian rank constraints. Neural Processing Letters, 54(4), 2645-2656.

[31] Xu, D., Ruan, C., Korpeoglu, E., Kumar, S., & Achan, K. (2020). Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962.