论文标题
KGTK：用于大型知识图操作和分析的工具包
KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis
论文作者
论文摘要
知识图（KGS）已成为代表，共享和添加知识的首选技术。尽管KG已成为主流技术，但用于大规模运营的RDF/SPARQL中心工具集是异质的，难以集成的，并且仅涵盖数据科学应用中通常需要的一部分操作。在本文中，我们提出了一个以数据科学为中心的工具包KGTK，旨在代表，创建，转换，增强和分析KGS。 KGTK代表表格中的图形，并利用了为数据科学应用程序开发的流行库，使大量开发人员能够轻松地为其应用构建知识图形管道。我们用现实世界的场景说明了框架，在这些场景中，我们使用kgtk来整合和操纵大型kg，例如wikidata，dbpedia和conceptnet。
Knowledge graphs (KGs) have become the preferred technology for representing, sharing and adding knowledge to modern AI applications. While KGs have become a mainstream technology, the RDF/SPARQL-centric toolset for operating with them at scale is heterogeneous, difficult to integrate and only covers a subset of the operations that are commonly needed in data science applications. In this paper we present KGTK, a data science-centric toolkit designed to represent, create, transform, enhance and analyze KGs. KGTK represents graphs in tables and leverages popular libraries developed for data science applications, enabling a wide audience of developers to easily construct knowledge graph pipelines for their applications. We illustrate the framework with real-world scenarios where we have used KGTK to integrate and manipulate large KGs, such as Wikidata, DBpedia and ConceptNet.
