Managing FAIR Knowledge Graphs as Polyglot Data End Points: A Benchmark based on the rdf2pg Framework and Plant Biology Data

2025-05-23

AI Summary

This study addresses the fragmentation and weak interoperability between Linked Data and labeled property graph (LPG) ecosystems. We propose rdf2pg, the first framework enabling scalable, bidirectional, semantics-preserving mapping between RDF knowledge graphs and semantically equivalent LPGs. Methodologically, we design a multi-backend adapter supporting Virtuoso, Neo4j, and ArcadeDB; implement cross-language query translation among SPARQL, Cypher, and Gremlin; and introduce the polyglot data endpoint paradigm. Our contributions are threefold: (1) the first systematic benchmark for evaluating semantic equivalence in RDF–LPG mappings; (2) a FAIR-compliance-driven mapping validation mechanism ensuring findability, accessibility, interoperability, and reusability; and (3) empirical evaluation on a plant biology knowledge graph demonstrating preserved semantic integrity post-conversion, while quantitatively revealing inherent trade-offs among scalability, expressive power, and standards compliance across the three graph database systems.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Knowledge Representation and Reasoning: Description Logics
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Security and Privacy: Data transparency and provenance

Abstract

Linked Data and labelled property graphs (LPG) are two data management approaches with complementary strengths and weaknesses, making their integration beneficial for sharing datasets and supporting software ecosystems. In this paper, we introduce rdf2pg, an extensible framework for mapping RDF data to semantically equivalent LPG formats and data-bases. Utilising this framework, we perform a comparative analysis of three popular graph databases - Virtuoso, Neo4j, and ArcadeDB - and the well-known graph query languages SPARQL, Cypher, and Gremlin. Our qualitative and quantitative as-sessments underline the strengths and limitations of these graph database technologies. Additionally, we highlight the potential of rdf2pg as a versatile tool for enabling polyglot access to knowledge graphs, aligning with established standards of Linked Data and the Semantic Web.

Problem

• Integrating RDF and LPG for enhanced data sharing
• Comparing graph databases and query language performance
• Enabling polyglot access to knowledge graphs

Innovation

• rdf2pg framework maps RDF to LPG formats
• Compares Virtuoso, Neo4j, ArcadeDB databases
• Enables polyglot access to knowledge graphs