🤖 AI Summary
This work addresses the pervasive ambiguity of concepts in knowledge graphs arising from domain-specific interpretations, a challenge inadequately handled by existing approaches that treat domain as external metadata and thus fail to enforce assertion validity at the representation level. The paper proposes the Domain-Contextualized Concept Graph (DCG) framework, which uniquely integrates domain as an intrinsic structural component of knowledge representation. By leveraging Kripke-style semantics and a compact predicate system, DCG embeds domain context directly into relational representations, enabling truth evaluation, inference, and conflict detection within specific domains. The framework supports concept disambiguation, domain-specific assertion validation, and explicit cross-domain relation linking, while providing formal mappings to RDF, OWL, and relational databases. This yields a computable, structurally grounded mechanism for domain-aware constraints, effectively mitigating knowledge system failures caused by neglecting domain context.
📝 Abstract
Knowledge graphs store large numbers of relations efficiently, but they remain weak at representing a quieter difficulty: the meaning of a concept often shifts with the domain in which it is used. A triple such as Apple, instance-of, Company may be acceptable in one setting while being misleading or unusable in another. In most current systems, domain information is attached as metadata, qualifiers, or graph-level organization. These mechanisms help with filtering and provenance, but they usually do not alter the formal status of the assertion itself. This paper argues that domain should be treated as part of knowledge representation rather than as supplementary annotation. It introduces the Domain-Contextualized Concept Graph (DCG), a framework in which domain is written into the relation and interpreted as a modal world constraint. In the DCG form (C, R at D, C'), the marker at D identifies the world in which the relation holds. Formally, the relation is interpreted through a domain-indexed necessity operator, so that truth, inference, and conflict checking are all scoped to the relevant world. This move has three consequences: ambiguous concepts can be disambiguated at the point of representation; invalid assertions can be challenged against their domain; cross-domain relations can be connected through explicit predicates. The paper develops this claim through a Kripke-style semantics, a compact predicate system, a Prolog implementation, and mappings to RDF, OWL, and relational databases. The contribution is a representational reinterpretation of domain itself. The central claim is that many practical failures in knowledge systems begin when domain is treated as external to the assertion. DCG addresses that by giving domain a structural and computable role inside the representation.