RDFFrames：用于机器学习工具的知识图访问
RDFFrames: Knowledge Graph Access for Machine Learning Tools
摘要：
表示为RDF数据集的知识图是许多机器学习应用程序的组成部分。RDF得到了一个丰富的数据管理系统和工具生态系统的支持，最值得注意的是提供SPARQL查询接口的RDF数据库系统。令人惊讶的是，用于知识图谱的机器学习工具并不使用SPARQL，尽管使用数据库系统具有明显的优势。这是由于SPARQL和机器学习工具在数据模型和编程风格方面的不匹配。机器学习工具处理表格格式的数据，并使用命令式编程风格进行处理，而SPARQL是声明式的，其基本操作是将图模式匹配到RDF三元组。我们认为，一个好的接口，知识图从机器学习软件栈应该使用一个命令式的，导航编程范式的基础上图遍历，而不是SPARQL查询范式的基础上图模式。在本文中，我们提出了RDFFrames，一个框架，提供了这样一个接口。RDFFrames提供了一个命令式的Python API，可以在内部转换为SPARQL，并且它与PyData机器学习软件栈集成。RDFFrames使用户能够进行一系列Python调用，以定义要从存储在RDF数据库系统中的知识图谱中提取的数据，并将这些调用转换为紧凑的SPQARL查询，在数据库系统上执行，并以标准表格格式返回结果。因此，RDFFrames是一个有用的数据准备工具，它将PyData的可用性与RDF数据库系统的灵活性和性能相结合。
英文摘要：
Knowledge graphs represented as RDF datasets are integral to many machine learning applications. RDF is supported by a rich ecosystem of data management systems and tools, most notably RDF database systems that provide a SPARQL query interface. Surprisingly, machine learning tools for knowledge graphs do not use SPARQL, despite the obvious advantages of using a database system. This is due to the mismatch between SPARQL and machine learning tools in terms of data model and programming style. Machine learning tools work on data in tabular format and process it using an imperative programming style, while SPARQL is declarative and has as its basic operation matching graph patterns to RDF triples. We posit that a good interface to knowledge graphs from a machine learning software stack should use an imperative, navigational programming paradigm based on graph traversal rather than the SPARQL query paradigm based on graph patterns. In this paper, we present RDFFrames, a framework that provides such an interface. RDFFrames provides an imperative Python API that gets internally translated to SPARQL, and it is integrated with the PyData machine learning software stack. RDFFrames enables the user to make a sequence of Python calls to define the data to be extracted from a knowledge graph stored in an RDF database system, and it translates these calls into a compact SPQARL query, executes it on the database system, and returns the results in a standard tabular format. Thus, RDFFrames is a useful tool for data preparation that combines the usability of PyData with the flexibility and performance of RDF database systems.
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。
