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.
Mental health detection from social media has gained increasing attention due to the spontaneous expression of psychological states on online platforms. However, accurate interpretation remains challenging because social media language is highly contextual, ambiguous, and often includes sarcasm, metaphors, or non-clinical expressions, which can lead to unreliable predictions. Existing approaches based on machine learning, deep learning, transformers, and large language models (LLMs) primarily rely on text-driven patterns and often lack explicit ontology-guided grounding, structured knowledge validation, and interpretability. Although LLMs improve contextual reasoning, their predictions can be inconsistent and prone to hallucination when not supported by structured domain knowledge and a unified cross-component validation mechanism. To address these limitations, this paper proposes an ontology-guided framework that integrates a Knowledge Graph (KG), an Ontology-Informed Retrieval Classifier (ORC), and a Large Language Model for interpretable mental health detection from social media text. The framework leverages the Human Phenotype Ontology (HPO) to guide symptom extraction and Knowledge Graph construction, normalizing informal linguistic expressions into ontology-grounded representations. A key design contribution is an explicit KG–ORC cross-validation consistency gate, which requires both the Knowledge Graph and the ORC module to independently reach agreement before a prediction is committed reducing erroneous inferences from either component alone. The LLM is deliberately restricted to a post-validation role, generating human-readable explanations and supportive recommendations only from validated outputs, thereby reducing hallucination risks and preserving knowledge-consistency. The proposed framework is evaluated on the Dreaddit dataset, a large-scale, multi-domain Reddit corpus for stress and mental health analysis. Empirical evaluation on the HPO guided symptom extraction confirms differential detection rates of 56.04% in stress positive posts versus 31.11% in stress negative posts, validating the signal relevance of the ontology-driven extraction. End-to-end binary classification on the held-out test split achieves an accuracy of 74.82% with an F1-score of 75.64%. Crucially, on the 66.55% of instances where the KG and ORC modules reach consensus, the consistent subset accuracy rises to 78.84% (F1 = 78.28%), compared to 63.03% for the Knowledge Graph alone. The KG–ORC agreement rate across the full test set is 82.02%. These results demonstrate 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
Summary Biomedical information extraction (BIE) is fundamental for transforming unstructured biomedical text into structured, computable knowledge, yet the effectiveness of large language models (LLMs) remains limited by dataset heterogeneity and lack of unified benchmarks. We present InfoFlowEX, a unified framework for generalizable biomedical knowledge extraction with LLMs. InfoFlowEX incorporates an automated data integration pipeline using ontology-guided alignment to construct BIE-Corpus, a large-scale multi-domain benchmark unifying 40 public datasets for named entity recognition and relation extraction. We further introduce a task-conditioned schema instruction tuning strategy encoding 28 biomedical entity and relation types into a schema codebase, enabling LLMs to align heterogeneous annotations and generalize across settings. Finally, we evaluated InfoFlowEX in diverse applications, including evidence retrieval for question-answering, clinical diagnosis from electronic health records, and knowledge graph expansion. 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 rapid growth of unstructured textual data necessitates automated approaches for transforming such information into structured, machine-readable knowledge. Knowledge Graphs (KGs) provide an effective framework for representing entities and their relationships; however, existing methods often suffer from fragmented pipelines, limited semantic consistency, and challenges in handling domain-specific variations. This paper presents an intelligent and scalable approach for knowledge graph construction from semantically enriched keyword-based inputs derived from a context-aware extraction process. The proposed method employs a unified pipeline comprising entity identification and ontology-based linking, context-aware relation extraction, and structured triple generation in the form of subject-predicate-object (SPO) representations. The generated triples are futher transformed into RDF format and organized into a coherent knowledge graph, followed by refinement steps to ensure semantic consistency and structural integrity. The approach is evaluated on representative datasets, including PubMed abstracts, and demonstrates improved performance in triplet extraction, entity and relation accuracy, and graph-level quality metrics such as density, clustering coefficient, and modularity. Comparative analysis with baseline methods highlights the effectiveness of the proposed approach in generating coherent and semantically enriched knowledge graphs. Additionally, the system exhibits strong scalability and computational efficiency, making it suitable for large-scale and real-world applications acreoss diverse domains. Overall, 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