Model-Independent Design of Knowledge Graphs

Abstract

Knowledge Graphs (KGs) can be seen as knowledge bases combining an extensional component, a database of facts, typically a property graph, and an intensional component, a formal specification of the available business experience, to derive new knowledge from those facts, often as new nodes and edges. Capitalizing on our experience in KGs and model management for the rollout of financial KGs for the Central Bank of Italy, in this work we present KGModel, a model-independent design framework for KGs. The framework adopts a meta-level approach: the data engineer visually designs the extensional component of the KG at a conceptual level and augments it with intensional specifications in MetaLog, a new logical model-independent language. This high-level specification of the KG is then translated into enforceable schema definitions for the target database and executable logical rules for a target reasoner. Our framework offers (i) a model-independent visual modeling language; (ii) MetaLog, a new language of the Datalog+/- family for the intensional component; (iii) new complementary software tools for the translation of meta-level specifications into their executable versions. We present the main ideas behind KGModel and show the suitability of the framework for real-world scenarios. This work is a short version of an EDBT 2022 paper.