Knowledge Graph and Large Language Model Fusion for Personalized Biomedical Literature Discovery
Keywords:
biomedical literature discovery, knowledge graphs, large language models, retrieval-augmented generation, personalization, governance, scientific information systemsAbstract
The biomedical literature is growing at a rate that makes manual curation and comprehensive literature review increasingly difficult. Personalized discovery systems must reconcile the breadth of scientific evidence with the specific informational needs of researchers, clinicians, and policy analysts. This paper presents a system-level examination of knowledge graph and large language model fusion for personalized biomedical literature discovery. It argues that neither symbolic knowledge organization nor parametric language generation alone is sufficient; their integration must be designed around structured biomedical relations, retrieval-augmented inference, user memory, and accountable deployment practices. The discussion begins with architectural foundations, considering graph encoding, language model query interpretation, and evidence grounding. It then addresses personalization mechanisms, including explicit preference models and implicit behavioral memory, and illustrates their use in discovery workflows for clinical and translational contexts. The paper further analyzes governance, safety, fairness, and sustainability implications, including hallucination control, privacy, reproducibility, and environmental cost. A system-level evaluation perspective is developed, emphasizing expert-in-the-loop assessment, continuous monitoring, and the separation of retrieval relevance from evidentiary authority. The conclusion identifies future directions for adaptive knowledge infrastructure that maintains scientific rigor while improving the efficiency of literature navigation. Throughout, the emphasis is on structural trade-offs and the institutional conditions required for trustworthy deployment in real biomedical settings.
References
1. Bornmann, L., & Mutz, R. (2015). Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references. Journal of the Association for Information Science and Technology, 66(11), 2215–2222. https://doi.org/10.1002/asi.23329
2. Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: Systematic review. Healthcare, 11(6), 887. https://doi.org/10.3390/healthcare11060887
3. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
4. Nicholson, D. N., & Greene, C. S. (2020). Constructing knowledge graphs and their biomedical applications. Computational and Structural Biotechnology Journal, 18, 1414–1428. https://doi.org/10.1016/j.csbj.2020.05.017
5. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
6. Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240. https://doi.org/10.1093/bioinformatics/btz682
7. Yu, X. (2026). Enhancing Search Efficiency through LLM-Based User Memory Systems for Query Matching and Intent Modeling.
8. Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2021). Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare, 3(1), Article 2. https://doi.org/10.1145/3458754
9. Himmelstein, D. S., Lizee, A., Hessler, C., Brueggeman, L., Chen, S. L., Hadley, D., Green, A., Khankhanian, P., Baranzini, S. E., & Greene, C. S. (2017). Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife, 6, e26726. https://doi.org/10.7554/eLife.26726
10. Roberts, K., Demner-Fushman, D., Voorhees, E. M., & Hersh, W. R. (2017). Overview of the TREC 2017 clinical trials track. In Proceedings of the Twenty-Sixth Text REtrieval Conference. National Institute of Standards and Technology.
11. Speretta, M., & Gauch, S. (2005). Personalized search based on user search histories. In Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (pp. 622–628). IEEE. https://doi.org/10.1109/WI.2005.114
12. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171–4186). Association for Computational Linguistics.
13. Wang, X., Gao, T., Zhu, Z., Zhang, Z., Liu, Z., Li, J., & Tang, J. (2021). KEPLER: A unified model for knowledge embedding and pre-trained language representation. Transactions of the Association for Computational Linguistics, 9, 176–194. https://doi.org/10.1162/tacl_a_00360
14. Yasunaga, M., Bosselut, A., Ren, H., Zhang, X., Manning, C. D., Liang, P., & Leskovec, J. (2022). Deep bidirectional language-knowledge graph pretraining. Advances in Neural Information Processing Systems, 35, 37309–37323.
15. Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y., Madotto, A., & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), Article 248. https://doi.org/10.1145/3571730
16. Kobsa, A. (2007). Privacy-enhanced personalization. Communications of the ACM, 50(8), 24–33. https://doi.org/10.1145/1278201.1278202
17. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220–229). ACM. https://doi.org/10.1145/3287560.3287596
18. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 3645–3650). Association for Computational Linguistics. https://doi.org/10.18653/v1/P19-1355
19. Chen, Q., Allot, A., & Lu, Z. (2021). LitCovid: An open database of COVID-19 literature. Nucleic Acids Research, 49(D1), D1534–D1540. https://doi.org/10.1093/nar/gkaa952
20. Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616(7956), 259–265. https://doi.org/10.1038/s41586-023-05881-4
21. Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human-AI interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1–13). ACM. https://doi.org/10.1145/3290605.3300233
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Engineering Systems and Digital Innovation
This work is licensed under a Creative Commons Attribution 4.0 International License.
