A layered reliability framework is defined in which graph-based inference addresses knowledge incompleteness, retrieval-augmented prompt control mitigates instability, and ontology grounding reduces semantic ambiguity, providing a foundation for more reliable biomedical AI systems.
Abstract
Biomedical knowledge discovery and clinical natural language processing increasingly rely on foundation models, but these approaches face persistent challenges, including incomplete curated knowledge, instability in language model outputs, and ambiguity in clinical interpretation. This dissertation argues that reliable biomedical AI requires integrating structured knowledge and foundation models through complementary control mechanisms rather than relying on any single method. To support this argument, the dissertation presents a unified framework across molecular and clinical domains. It develops an iterative prompt refinement approach with retrieval-augmented generation to improve the reliability of biomedical relation extraction, introduces graph transformer models to infer gene–gene relations and augment incomplete pathway knowledge, and proposes ontology-grounded retrieval and normalization pipelines to improve semantic alignment in multidisciplinary clinical notes. Together, these contributions define a layered reliability framework in which graph-based inference addresses knowledge incompleteness, retrieval-augmented prompt control mitigates instability, and ontology grounding reduces semantic ambiguity, providing a foundation for more reliable biomedical AI systems.
An ontology-guided framework that integrates a Knowledge Graph, an Ontology-Informed Retrieval Classifier, and a Large Language Model for interpretable mental health detection from social media text demonstrates that the KG–ORC cross-validation gate measurably improves predictive reliability over single component baselines, and that ontology-guided, knowledge-grounded reasoning offers a principled path toward interpretable and knowledge-consistent mental health analysis from social media.
Amina Tahir, Ghulam Mustafa, Muhammad Tanvir Afzal et al.· Social Network Analysis and...· 0 citations
Results demonstrate that InfoFlowEX equips LLMs with robust adaptability, achieving consistent gains over baselines with minimal task-specific customization, highlighting InfoFlowEX for real-world biomedical applications.
Wuyang Lan, Siqi Zhang, Wenzheng Wang et al.· Cell Reports Medicine· 0 citations
Adapting large language models (LLMs) to new domains using techniques like pre-training or fine-tuning can be computationally intensive. This challenge has motivated researchers and practitioners to explore alternative strategies, such as Retrieval-based augmentation mechanisms, to reduce training cost while preserving model adaptability. These mechanisms enable LLMs to generate responses grounded in external and previously unseen data. Among these approaches, RetrievalAugmented Generation (RAG) is the widely adopted due to its relatively low computational overhead. However, conventional RAG pipelines exhibit some limitations, including weak semantic coherence across retrieved contexts, limited structural reasoning capability, and the absence of explicit relational representations. To overcome these limitations, recent research has shifted toward the integration of Knowledge Graphs (KGs), which provide structured semantic representations and explicit relational modeling. KG-based approaches looks promising but the typically depend on manually designed ontologies, resulting in scalability constraints and domain adaptation challenges. To address these limitations, we propose OF-BIO-KG, a framework for automated, ontology-free knowledge graph construction. Our approach begins by projecting domain data into a vector database to identify semantically coherent clusters. An LLM then labels these clusters as conceptual nodes and infers both hierarchical and cross-cluster relationships. These nodes and links are persisted in a graph database. By eliminating the need for predefined schemas, our framework offers a scalable, computationally efficient solution for domain-specific LLM adaptation.
Ahlem Ben Younes, Baha Eddine Kalai, Laila Ben Ayed et al.· Annual International Compute...· 0 citations
Initial experiments on heart-failure-focused clinical question answering show that CGX improves evidence retrieval quality and perceived answer reliability over conventional retrieval methods, while reducing total graph construction time by 69.7% under the same input corpus and hardware setting.
Dat Nguyen, Anh N. Le, Binh T. D. Trinh et al.· Journal of Biomedical Inform...· 0 citations
This work presents a scalable, reproducible framework for evaluating, optimizing, and interpreting LLMs for biomedical knowledge extraction, with a focus on gene–gene regulatory relation prediction, pathway component recognition, multimodal pathway figure understanding, and automated prompt optimization.
The proposed approach effectively bridges the gap between unstructured text and structured knowledge representation, enabling reliable, scalable, and high-quality knowledge graph construction.
Avinash Gondal, Sunil Wankhade· Dandao Xuebao/Journal of Bal...· 0 citations
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