# A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields
## 2 Background and Prior Work  
Knowledge representation (KR) plays a pivotal role in enabling entities to predict outcomes, form structured perceptions of the world, establish foundations for intelligent reasoning, facilitate efficient computations, and serve as a medium for human expression [8]. KR methods are extensively applied across diverse domains, including expert systems, artificial intelligence, and robotics. Many applications in these areas rely on KRmethodologies for representation and reasoning [9] and are applied in various fields such as education, science, engineering, finance, and healthcare [10]. Prominent KR methodologies include knowledge graphs, ontologies, and semantic technologies. Knowledge graphs (KGs), also referred to as semantic graphs, encode relationships between entities within graph structures, providing semantically structured data that support various applications, such as question answering, recommendation systems [11], and information retrieval [12]. KGs are instrumental in advancing intelligent systems by enabling machines to process and utilize interconnected information effectively. Ontologies, defined as explicit machine-interpretable specifications of conceptualizations, describe entities within a domain, their attributes, and their interrelationships [13]. They provide a shared vocabulary for humans and machines, facilitating consistent information sharing, systematic analysis, and reuse of domain knowledge [14]. Ontologies are usually formulated in two main languages: RDF and OWL [15]. Semantic technologies extract meaning from data by integrating and managing diverse information streams. With ontologies often represented as graphs, advanced graph algorithms can infer insights by modeling domain knowledge and data relationships. In the context of healthcare, KR methods such as KGs and ontologies play a crucial role in representing complex medical relationships across multiple dimensions, including disease diagnosis, monitoring, and treatment, enabling precise decision making and inference within clinical frameworks [16]. Recent advancements in KR include integrating machine learning techniques with KGs, enabling dynamic updates, and improving inference capabilities. Additionally, KR serves as a cornerstone for explainable AI [17] by offering interpretable domain knowledge models. Organizing medical knowledge with KR methodologies defines rules and relationships across various contexts, providing essential decision support in monitoring scenarios. This approach helps medical staff interpret data effectively, leading to informed decisions that enhance patient care [18]. In addition, diagnosing diseases relies on identifying specific illnesses through established classifications, supporting accurate and reliable diagnoses [19]. A methodology for constructing a graph of disease-symptom relationships, using data from 273 K patient records, is presented in [3]. Recent advancements in clinical decision-support systems have increasingly utilized KGs to improve diagnostic accuracy and deliver personalized care. For instance, constructing and evaluating causal KGs for diabetic nephropathy has enhanced clinical decision making by modeling intricate causal relationships within patient data [20]. Similarly, integrating KGs with large language models has been investigated to support real-time emergency decision making in critical care scenarios [21]. Clinical KGs incorporating proteomics data have also demonstrated potential in personalized medicine, enabling more precise diagnostics and customized treatments [22]. Moreover, enriching KGs with clinical narratives through natural language processing (NLP), named entity recognition (NER), and biomedical ontologies has improved the extraction and organization of critical healthcare information [23]. These advancements highlight the transformative role of KGs in augmenting clinical decision-support systems, especially when combined with semantic technologies and patient-specific data.