Model-Independent Design of Knowledge Graphs - Lessons Learnt From Complex Financial Graphs

We propose a model-independent design framework for Knowledge Graphs (KGs), capitalizing on our experience in KGs and model management for the roll out of a very large and complex financial KG for the Central Bank of Italy. KGs have recently garnered increasing attention from industry and are currently exploited in a variety of applications. Many of the common notions of KG share the presence of an extensional component, typically implemented as a graph database storing the enterprise data, and an intensional component, to derive new implicit knowledge in the form of new nodes and new edges. Our framework, KGModel, is based on a meta-level approach, where the data engineer designs the extensional and the intensional components of the KG—the graph schema and the reasoning rules, respectively—at meta-level. Then, in a model-driven fashion, such high-level specification is translated into schema definitions and reasoning rules that can be deployed into the target database systems and state-of-the-art reasoners. Our framework offers a model-independent visual modeling language, a logic-based language for the intensional component, and a set of new complementary software tools for the translation of metalevel specifications for the target systems. We present the details of KGModel, illustrate the software tools we implemented and show the suitability of the framework for real-world scenarios.