Mapping Patterns for Virtual Knowledge Graphs
This work provides a structured approach to managing mapping definitions for developers and researchers working with Virtual Knowledge Graphs, addressing a critical pain point in data integration.
The paper addresses the bottleneck of defining, validating, and maintaining mappings in Virtual Knowledge Graphs (VKG) by proposing a comprehensive catalog of sophisticated mapping patterns. This catalog is built upon established methodologies from data management, analysis, and conceptual modeling, extended through analysis of VKG benchmarks and real-world use cases, and validated to cover the vast majority of patterns in these scenarios.
Virtual Knowledge Graphs (VKG) constitute one of the most promising paradigms for integrating and accessing legacy data sources. A critical bottleneck in the integration process involves the definition, validation, and maintenance of mappings that link data sources to a domain ontology. To support the management of mappings throughout their entire lifecycle, we propose a comprehensive catalog of sophisticated mapping patterns that emerge when linking databases to ontologies. To do so, we build on well-established methodologies and patterns studied in data management, data analysis, and conceptual modeling. These are extended and refined through the analysis of concrete VKG benchmarks and real-world use cases, and considering the inherent impedance mismatch between data sources and ontologies. We validate our catalog on the considered VKG scenarios, showing that it covers the vast majority of patterns present therein.