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

TL;DR

This paper presents rdf2pg, a framework for converting RDF data into LPG formats, and compares three graph databases and query languages to evaluate their performance and suitability for FAIR knowledge graphs in plant biology.

Contribution

The paper introduces rdf2pg, an extensible framework for RDF to LPG mapping, and provides a comprehensive benchmark of graph databases and query languages for FAIR knowledge graphs.

Findings

Virtuoso, Neo4j, and ArcadeDB show distinct strengths and limitations.

rdf2pg enables effective polyglot access to knowledge graphs.

Benchmark results guide optimal database and query language selection.

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.