🤖 AI Summary
Traditional knowledge graphs are constrained by static ontologies, limiting their capacity for context-sensitive concept representation and cross-domain reasoning. To address this, we propose Domain-Contextualized Concept Graphs (CDC), which explicitly model “domain” as a computable element of inference, thereby relaxing the rigidity of conventional ontologies. Our method introduces the C-D-C (Concept–Domain–Concept) ternary structure, where domain serves as a relational modifier, and establishes semantic mapping principles grounded in cognitive linguistics. Full logical inference is implemented in Prolog, supported by over twenty standardized relation predicates spanning structural, logical, cross-domain, and temporal dimensions. Empirical validation across educational systems, enterprise knowledge management, and technical documentation demonstrates that CDC enables context-aware reasoning, personalized knowledge modeling, and cross-domain analogy—significantly enhancing both the adaptability and cognitive plausibility of knowledge graphs.
📝 Abstract
Traditional knowledge graphs are constrained by fixed ontologies that organize concepts within rigid hierarchical structures. The root cause lies in treating domains as implicit context rather than as explicit, reasoning-level components. To overcome these limitations, we propose the Domain-Contextualized Concept Graph (CDC), a novel knowledge modeling framework that elevates domains to first-class elements of conceptual representation. CDC adopts a C-D-C triple structure - <Concept, Relation@Domain, Concept'> - where domain specifications serve as dynamic classification dimensions defined on demand. Grounded in a cognitive-linguistic isomorphic mapping principle, CDC operationalizes how humans understand concepts through contextual frames. We formalize more than twenty standardized relation predicates (structural, logical, cross-domain, and temporal) and implement CDC in Prolog for full inference capability. Case studies in education, enterprise knowledge systems, and technical documentation demonstrate that CDC enables context-aware reasoning, cross-domain analogy, and personalized knowledge modeling - capabilities unattainable under traditional ontology-based frameworks.