P2KG: Declarative Construction and Quality Evaluation of Knowledge Graph from Polystores

Xiuwen Zheng, Subhasis Dasgupta, Amarnath Gupta

Abstract: Constructing knowledge graphs from heterogeneous data sources and evaluating their quality and consistency are important research questions in the field of knowledge graph. We propose mapping rules to guide users to translate data from relational and graph sources into a meaningful knowledge graph, and design a user-friendly language to specify the mapping rules. Given the mapping rules and constraints on source data, equivalent constraints on the target graph can be inferred, which is referred as data source constraints. Besides this type of constraints, we design other two types: user-specified constraints and general rules that a high-quality knowledge graph should adhere to. We translate the three types of constraints into uniform expressions in the form of graph functional dependencies and extended graph dependencies, which can be used for consistency checking. Our approach provides a systematic way to build and evaluate knowledge graphs from diverse data sources.