{"ID":2853134,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16802","arxiv_id":"2510.16802","title":"Domain-Contextualized Concept Graphs: A Computable Framework for Knowledge Representation","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 - \u003cConcept, Relation@Domain, Concept'\u003e - 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.","short_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...","url_abs":"https://arxiv.org/abs/2510.16802","url_pdf":"https://arxiv.org/pdf/2510.16802v1","authors":"[\"Chao Li\",\"Yuru Wang\"]","published":"2025-10-19T11:53:10Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}