An Outline of Information Ontology: From Difference, Observation, and Distinction to Triadic Information Association

This paper attempts to establish a foundational theoretical framework for information ontology starting from difference, observation, and distinction, and further discusses its relationship with cognition, logic, mathematics, and artificial intelligence. The paper proposes that information is a natural property possessed by existence, rather than a product created by observation. The role of observation and cognition is to extract, distinguish, and organize information, not to confer upon it its original existence. For any existence that can enter human cognition, the paper proposes a triadic structure of content, state, and relation, and argues for its necessity for human cognition through a chain of reasoning: cognition necessarily has an object; an object necessarily has content; cognizable content necessarily has a cognizable state; and there necessarily exists a relation between content and state. Furthermore, the paper argues that triadic information structures are not isolated, but can recursively nest and form a non-hierarchical associative network. Associations in the network are not limited to direct connections between two objects; they can also be established through intermediate information structures. When expressing final results, a cognitive subject may omit the intermediate associative processes that actually formed the result. Therefore, surface expression cannot fully represent the information associations that occurred during cognition. On this basis, the paper attempts to reinterpret logic and mathematics: logic can be examined from the associability between information structures, while mathematics involves the transformation and association of information structures. The existence of information association, the conditions for association to hold, and the cognitive subject's judgment of correctness regarding the result need to be distinguished. The paper treats correctness as a state categorization applied by the cognitive subject to information, rather than equating "being judged as correct" with objective truth itself. Finally, the paper discusses the potential implications of this framework for artificial intelligence, proposing a hypothesis to be tested: the reliable cognition and logical capabilities of AI may depend not only on the quantity of information and computational scale, but also on whether the system can form, maintain, and verify information association networks with appropriate structure. This paper is currently an exploratory theoretical outline. The core propositions proposed herein still require further examination through conceptual analysis, formal modeling, and empirical research.