Declarative RDF graph generation from heterogeneous (semi-)structured data: A systematic literature review

Abstract

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.