The exponential growth of information in the digital era has produced an unprecedented paradox: although the global volume of data expands at extraordinary rates each year, the average semantic value carried by individual units of information continues to decline. This imbalance has generated a condition of elevated epistemic entropy, in which meaning, coherence, and interpretability are increasingly diluted by informational excess. The central challenge of contemporary knowledge systems, therefore, is no longer the scarcity of information, but the absence of effective structural organization capable of transforming raw data into coherent understanding. This paper proposes a theoretical framework for Universal Automatic Knowledge Structuring (UAKS), a system designed to convert unstructured and heterogeneous information into hierarchically organized, semantically coherent knowledge through principles of entropy reduction. Drawing upon information theory, cognitive science, and knowledge graph architectures, we argue that meaningful knowledge emerges not from data accumulation, but from the imposition of structure, hierarchy, and relational context. The proposed framework introduces a hybrid architectural approach that integrates statistical learning mechanisms with formal ontological reasoning. Such a system is capable of autonomously extracting fundamental concepts from diverse data sources, establishing hierarchical dependencies and causal relationships among them, identifying logical inconsistencies across disciplinary boundaries, and dynamically reorganizing knowledge structures as new information becomes available. This dynamic adaptability ensures that the knowledge representation remains both current and internally consistent over time. Our analysis demonstrates that an effective automatic structuring system must operate at the intersection of entropy minimization and semantic coherence maximization. In this context, entropy reduction is not merely a technical objective, but a cognitive necessity for sustaining intelligibility in an environment of overwhelming informational abundance. We discuss theoretical criteria for evaluating the quality of structured knowledge, outline algorithmic strategies for automatic hierarchy construction, and examine the computational and epistemological challenges inherent in developing such systems. By establishing a foundational blueprint for automated knowledge structuring, this work addresses one of the defining intellectual challenges of the twenty-first century: transforming information abundance into structured understanding and, ultimately, actionable wisdom.
Zen Revista (Thu,) studied this question.