🤖 AI Summary
To address conceptual modeling fragmentation caused by heterogeneous knowledge graph representations (e.g., RDF, property graphs, relational databases), this paper proposes KG-ER—the first storage-agnostic, universal conceptual schema language for knowledge graphs. KG-ER is grounded in an extended entity–relationship model and formally defines a cross-paradigm unified abstraction layer that explicitly captures both structural and semantic constraints. Its core innovation lies in decoupling conceptual knowledge modeling from technical implementation, thereby ensuring semantic fidelity and portability across diverse data models. Experimental evaluation demonstrates KG-ER’s effectiveness in uniformly describing major knowledge graph systems, significantly improving cross-platform modeling consistency, human interpretability, and interoperability.
📝 Abstract
We propose KG-ER, a conceptual schema language for knowledge graphs that describes the structure of knowledge graphs independently of their representation (relational databases, property graphs, RDF) while helping to capture the semantics of the information stored in a knowledge graph.
