Both the notion of Property Graphs (PG) and the Resource Description Framework (RDF) are commonly used models for representing graph-shaped data. While there exist some system-specific solutions to convert data from one model to the other, these solutions are not entirely compatible with one another and none of them appears to be based on a formal foundation. In fact, for the PG model, there does not even exist a commonly agreed-upon formal definition.
The aim of this document is to reconcile both models formally. To this end, the document proposes a formalization of the PG model and introduces well-defined transformations between PGs and RDF. As a result, the document provides a basis for the following two innovations: On one hand, by implementing the RDF-to-PG transformations defined in this document, PG-based systems can enable their users to load RDF data and make it accessible in a compatible, system-independent manner using, e.g., the graph traversal language Gremlin or the declarative graph query language Cypher. On the other hand, the PG-to-RDF transformation in this document enables RDF data management systems to support compatible, system-independent queries over the content of Property Graphs by using the standard RDF query language SPARQL. Additionally, this document represents a foundation for systematic research on relationships between the two models and between their query languages.
On Reasoning with RDF Statements about Statements using Singleton Property Triples
Kno.e.sis Center, Wright State University（莱特州立大学Kno.e.sis中心）；National Library of Medicine, National Institute of Health（美国国立卫生研究院国家医学图书馆）；National Center of Biotechnology Information, National Institute of Health（美国国立卫生研究院国家生物技术信息中心）；Universitat Pompeu Fabra（庞培法布拉大学）；Stanford Center for Biomedical Informatics, Stanford University（斯坦福大学生物医学信息学中心）
Semantic Property Graph for Scalable Knowledge Graph Analytics
图是描述各种复杂系统的活动、关系和演化的自然和基本表示。许多领域，如通信，引用，采购，生物学，社交媒体和运输，可以建模为一组实体及其关系。资源描述框架（RDF）和标记属性图（LPG）是两种最常用的数据模型，用于在图中编码信息。这两种模型在使用基本图形元素（如节点和边）方面是相似的，但在建模方法、可表达性、序列化和目标应用程序方面有所不同。RDF是一种灵活的数据交换模型，用于表达有关实体的信息，但它往往具有较高的内存占用和低效的存储，这并不使其成为执行可扩展图分析的自然选择。相比之下，LPG在执行可扩展的图分析任务（如子图匹配、网络对齐和实时知识图谱查询）中作为可靠的模型获得了关注。它提供了高效的存储、快速的遍历和灵活性，可以对各种现实领域进行建模。同时，LPG缺乏诸如本体之类的正式知识表示的支持来提供自动化知识推理。我们提出语义属性图（SPG）作为一个逻辑投影的具体RDF到LPG模型。SPG继续使用RDF本体来定义投影图的类型层次结构，并根据给定的本体对其进行验证。我们提出了一个框架，使用两种不同的计算环境转化为SPG的具体RDF图。我们还使用Amazon Web Services提供基于云的图形迁移功能。
Managing FAIR Knowledge Graphs as Polyglot Data End Points: A Benchmark based on the rdf2pg Framework and Plant Biology Data
Rothamsted Research（洛桑研究所）；Aragon Institute of Engineering Research (I3A)（阿拉贡工程研究所（I3A））；University of Zaragoza（萨拉戈萨大学）；Arcade Data Ltd（Arcade Data 有限公司）
Free University of Bozen-Bolzano, Italy（博洛尼亚-博兹纳自由大学，意大利）；Université Paris-Saclay, CNRS, LISN, France（巴黎-萨克雷大学，法国 CNRS LISN）；Birkbeck, University of London, UK（伦敦大学伯克贝克学院，英国）；University of Huddersfield, UK（赫德斯菲尔德大学，英国）；Relational AI, Berkeley, CA, USA（关系AI，美国加州伯克利）；Hasselt University, Hasselt, Belgium（哈塞尔特大学，比利时）；University of Wrocław, Poland（沃林福大学，波兰）
Transforming Shape Schemas with Composable Property-Graph Queries (Extended Version)
The Software Languages Team（软件语言团队）；University of Koblenz（科伦茨大学）；Institute for Artificial Intelligence（人工智能研究所）；University of Stuttgart（斯图加特大学）；University of Southampton（南安普顿大学）
TU Wien（维也纳技术大学）；Univ. Lille（里尔大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Centrale Lille（里尔中央理工学院）；Birkbeck, University of London（伦敦大学伯贝克学院）；University of Oviedo（奥维耶多大学）；University of Bayreuth（拜罗伊特大学）；Università di Napoli Federico II（那不勒斯费德里科二世大学）；University of Warsaw（华沙大学）；Paderborn University（帕德博恩大学）；WU Wien（维也纳经济大学）；CSH Vienna（维也纳计算机科学中心）；Free University of Bolzano（波尔扎诺自由大学）；University of Bialystok（比亚韦斯托克大学）
