FunMap：用于知识图谱创建的功能映射的高效执行
FunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation
摘要：
在过去的几年里，数据呈指数级增长，知识图谱构成了强大的形式主义，可以集成无数现有的数据源。转换函数--由基于函数的映射语言（如FunUL和RML+FnO）指定--可以应用于克服跨异构数据源的互操作性问题。然而，缺乏有效执行这些映射语言的引擎阻碍了它们的全球采用。我们提出了FunMap，基于函数的映射语言的解释器，它依赖于一组无损重写规则来推下和物化的执行功能的初始步骤的知识图谱创建。虽然FunMap适用于任何支持映射规则之间连接的基于函数的映射语言，但FunMap的可行性在RML+FnO上显示。FunMap减少了数据冗余，例如，重复和未使用的属性，并将RML+FnO映射转换为一组可在RML兼容引擎上执行的等效规则。我们评估FunMap的性能超过现实世界的测试平台，从生物医学领域。结果表明，FunMap将RML兼容引擎的执行时间减少了18倍，从而为知识图谱创建提供了可扩展的解决方案。
英文摘要：
Data has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions -- specified with function-based mapping languages like FunUL and RML+FnO -- can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RML-compliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation.
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。
