Enterprise knowledge graphs (EKGa) are a novel paradigm for consolidating and semantically integrating large numbers of heterogeneous data sources into a comprehensive dataspace. The main goal of an EKG is to provide a data layer that is semantically connected to enterprise data, so that applications can have integrated access to enterprise data sources through that semantic layer. To make legacy relational data sources accessible through the organization's knowledge graph, it is necessary to create an RDF view of the underlying relational data (RDB2RDF view). An RDB2RDF view can be materialized to improve query performance and data availability. However, a materialized RDB2RDF view must be continuously maintained to reflect updates over the relational database. This article proposes a formal framework for constructing the materialized data graph for an RDB2RDF view and for incrementally maintaining the view's data graph. The article also presents an architecture and algorithms for implementing the proposed framework.

翻译：企业知识图谱（EKG）是一种将大量异构数据源整合并语义集成到统一数据空间的新范式。EKG的主要目标是提供一个与企业数据语义连接的数据层，使应用程序能够通过该语义层集成访问企业数据源。为使遗留关系数据源能够通过组织的知识图谱进行访问，需要为底层关系数据创建RDF视图（RDB2RDF视图）。RDB2RDF视图可通过物化方式提升查询性能与数据可用性。然而，物化的RDB2RDF视图必须持续维护以反映关系数据库的更新。本文提出了一个形式化框架，用于构建RDB2RDF视图的物化数据图，并对视图数据图进行增量维护。文中同时给出了实现该框架的体系结构与算法。