SDM-RDFizer：一个高效创建RDF知识图谱的RML解释器
SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs
摘要：
近年来，数据量呈指数级增长，并且知识图谱作为整合从无数数据源收获的数据和知识的数据结构而受到关注。然而，大容量、高重复率和异构性等数据复杂性问题通常表征这些数据源，需要数据管理工具能够解决这些问题对知识图谱创建过程的负面影响。在本文中，我们提出了SDM-RDFizer，RDF映射语言（RML）的解释器，将各种格式的原始数据转换为RDF知识图谱。SDM-RDFizer实现了新的算法来执行RML中映射之间的逻辑运算符，从而允许扩展到数据不仅广泛而且具有高重复率的复杂场景。我们经验性地评估了SDM-RDFizer对不同测试平台的性能，这些测试平台具有不同的数据量、重复和异构配置。观察到的结果表明，SDM-RDFizer是两个数量级的速度比最先进的，因此，这意味着SDM-RDFizer的知识图谱创建的互操作性和可扩展的解决方案。SDM-RDFizer通过Github仓库和DOI作为资源公开提供。
英文摘要：
In recent years, the amount of data has increased exponentially, and knowledge graphs have gained attention as data structures to integrate data and knowledge harvested from myriad data sources. However, data complexity issues like large volume, high-duplicate rate, and heterogeneity usually characterize these data sources, being required data management tools able to address the impact negatively of these issues on the knowledge graph creation process. In this paper, we propose the SDM-RDFizer, an interpreter of the RDF Mapping Language (RML), to transform raw data in various formats into an RDF knowledge graph. SDM-RDFizer implements novel algorithms to execute the logical operators between mappings in RML, allowing thus to scale up to complex scenarios where data is not only broad but has a high-duplication rate. We empirically evaluate the SDM-RDFizer performance against diverse testbeds with diverse configurations of data volume, duplicates, and heterogeneity. The observed results indicate that SDM-RDFizer is two orders of magnitude faster than state of the art, thus, meaning that SDM-RDFizer an interoperable and scalable solution for knowledge graph creation. SDM-RDFizer is publicly available as a resource through a Github repository and a DOI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
