MapSDI：一个面向知识图谱生成的语义数据集成框架
MapSDI: A Scaled-up Semantic Data Integration Framework for Knowledge Graph Creation
摘要：
语义Web技术为数据集成和知识图谱创建问题提供了有效的解决方案。然而，随着大数据在不同领域的快速增长，不同的互操作性问题仍然需要解决，可扩展性是主要挑战之一。在本文中，我们解决了大规模创建知识图谱的问题，并提供MapSDI，一个基于映射规则的框架，用于优化语义数据集成到知识图谱。MapSDI允许有效地对大型、异构和潜在低质量数据进行语义丰富。MapSDI的输入是一组数据源和由映射语言（如RML）生成的映射规则。首先，MapSDI根据从映射规则中提取的语义信息，通过执行基本的数据库操作符来预处理源;它投射出所需的属性，消除重复项，并选择相关条目。所有这些运算符都是基于映射规则编码的知识定义的，然后语义化引擎（或RDFizer）将使用这些映射规则来生成知识图谱。我们已经实证研究了MapSDI对现有RDFizer的影响，并观察到知识图谱创建时间平均可以减少一个数量级。它还表明，理论上，MapSDI提供的源和规则转换是数据无损的。
英文摘要：
Semantic web technologies have significantly contributed with effective solutions for the problems of data integration and knowledge graph creation. However, with the rapid growth of big data in diverse domains, different interoperability issues still demand to be addressed, being scalability one of the main challenges. In this paper, we address the problem of knowledge graph creation at scale and provide MapSDI, a mapping rule-based framework for optimizing semantic data integration into knowledge graphs. MapSDI allows for the semantic enrichment of large-sized, heterogeneous, and potentially low-quality data efficiently. The input of MapSDI is a set of data sources and mapping rules being generated by a mapping language such as RML. First, MapSDI pre-processes the sources based on semantic information extracted from mapping rules, by performing basic database operators; it projects out required attributes, eliminates duplicates, and selects relevant entries. All these operators are defined based on the knowledge encoded by the mapping rules which will be then used by the semantification engine (or RDFizer) to produce a knowledge graph. We have empirically studied the impact of MapSDI on existing RDFizers, and observed that knowledge graph creation time can be reduced on average in one order of magnitude. It is also shown, theoretically, that the sources and rules transformations provided by MapSDI are data-lossless.
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。
