arXiv:2508.02548v3 Announce Type: replace-cross 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.
The proliferation of knowledge graphs across various domains necessitates standardized and semantically rich schema languages to ensure interoperability and clearer data interpretation.
A robust conceptual schema language for knowledge graphs can significantly improve data integration, consistency, and the reliability of AI systems built upon them, crucial for enterprise and scientific applications.
This proposal offers a standardized way to describe knowledge graph structures, potentially simplifying development, reducing inconsistencies, and enhancing semantic understanding across different data representations.
- · Knowledge graph developers
- · Data scientists
- · AI researchers
- · Enterprises leveraging knowledge graphs
- · Organizations with siloed, non-standardized knowledge graph implementations
Improved interoperability and data quality in knowledge graph implementations.
Accelerated development of more sophisticated and reliable AI applications that rely on structured knowledge.
Enhanced automation and agentic capabilities due to more robust and semantically rich foundational knowledge bases for AI agents.
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Read at arXiv cs.AI