Semantic Reasoning in Medicine: The Role of Knowledge Graphs Across Five Key Domains

Knowledge graphs are gaining traction as a tool for integrating complex biomedical and clinical data, offering a structured way to reason across diseases, drugs, and patient records for improved decision-making and personalized care, according to a new survey of the field [1]. The survey, posted on the arXiv preprint server, maps the role of knowledge graphs (KGs) across five key medical domains: clinical decision support, disease and treatment outcome prediction, health recommender systems, precision medicine, and medical question answering [1]. By representing entities and their relationships, KGs provide a semantic backbone that can enhance interpretability and patient-specific reasoning [1]. The work also catalogs the pipelines used to build these graphs, drawing on electronic health records, clinical narratives, biomedical literature, and web resources through ontologies, semantic web technologies, and deep-learning-based information extraction [1]. Ontologies, a foundational component of such systems, are formal representations that define categories, properties, and relations within a domain, and are already used in biomedical informatics to improve data interoperability and discoverability [2]. Despite the promise, the review identifies persistent obstacles. Knowledge coverage remains limited and fragmented, and aligning data from heterogeneous sources is technically difficult [1]. Current reasoning and representation-learning methods can be fragile when applied to dense, multi-relational graphs, and unresolved questions around privacy, bias, and accountability continue to shadow deployment in clinical settings [1]. These challenges sit within a broader artificial intelligence landscape where techniques such as knowledge representation and reasoning have been core research goals since the field's founding in 1956, and where recent advances in deep learning and transformer architectures have accelerated capability but also intensified scrutiny around safety and ethical use [3]. The survey's analysis arrives as medical AI systems face the fundamental problem-solving demands of real-world healthcare, where solutions require sufficient resources and knowledge to overcome complex, often ill-defined obstacles [4]. The authors argue that hybrid neuro-symbolic pipelines—which combine neural learning with symbolic reasoning—represent one active area of development aimed at making KG-driven tools more robust [1]. The paper is available as an open-access preprint on arXiv, a repository that hosts over two million articles and receives roughly 24,000 submissions per month, though it has not yet undergone peer review [11].