将RDF知识图谱转为多语言图数据库，实现跨平台访问
Managing FAIR Knowledge Graphs as Polyglot Data End Points: A Benchmark based on the rdf2pg Framework and Plant Biology Data
- 提出rdf2pg框架，实现RDF与属性图的语义等价映射
- 对比Virtuoso、Neo4j、ArcadeDB在查询性能上的差异
- 适合需要多数据库协同的知识图谱研究者
链接数据与标注属性图（LPG）是两种互补的数据管理方式，其融合有助于数据共享与软件生态支持。本文提出rdf2pg框架，可将RDF数据映射为语义等价的属性图格式与数据库。基于此框架，我们对三种主流图数据库——Virtuoso、Neo4j和ArcadeDB，以及三种典型图查询语言——SPARQL、Cypher和Gremlin进行了定性与定量对比分析。结果揭示了各类技术的优势与局限性。同时，强调了rdf2pg作为多语言访问知识图谱的通用工具潜力，符合链接数据与语义网标准。
原文摘要 · Abstract (English)
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.
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