More and more data in various formats are integrated into knowledge graphs. However, there is no overview of existing approaches for generating knowledge graphs from heterogeneous (semi-)structured data, making it difficult to select the right one for a certain use case. To support better decision making, we study the existing approaches for generating knowledge graphs from heterogeneous (semi-)structured data relying on mapping languages. In this paper, we investigated existing mapping languages for schema and data transformations, and corresponding materialization and virtualization systems that generate knowledge graphs. We gather and unify 52 articles regarding knowledge graph generation from heterogeneous (semi-)structured data. We assess 15 characteristics on mapping languages for schema transformations, 5 characteristics for data transformations, and 14 characteristics for systems. Our survey paper provides an overview of the mapping languages and systems proposed the past two decades. Our work paves the way towards a better adoption of knowledge graph generation, as the right mapping language and system can be selected for each use case.

越来越多的各种格式的数据被集成到知识图谱中。但是，没有对从异构（半）结构化数据生成知识图谱的现有方法进行概述，因此很难为特定用例选择正确的方法。为了支持更好的决策，我们研究了依赖映射语言从异构（半）结构化数据生成知识图谱的现有方法。在本文中，我们研究了用于模式和数据转换的现有映射语言，以及生成知识图谱的相应物化和虚拟化系统。我们收集并统一了 52 篇关于从异构（半）结构化数据生成知识图谱的文章。我们评估了用于模式转换的映射语言的 15 个特征，数据转换有 5 个特征，系统有 14 个特征。我们的调查报告概述了过去二十年提出的映射语言和系统。我们的工作为更好地采用知识图谱生成铺平了道路，因为可以为每个用例选择正确的映射语言和系统。