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Knowledge graphs and ontologies provide promising technical solutions forimplementing the FAIR Principles for Findable, Accessible, Interoperable, andReusable data and metadata. However, they also come with their own challenges.Nine such challenges are discussed and associated with the criterion ofcognitive interoperability and specific FAIREr principles (FAIR + Explorabilityraised) that they fail to meet. We introduce an easy-to-use, open sourceknowledge graph framework that is based on knowledge graph building blocks(KGBBs). KGBBs are small information modules for knowledge-processing, eachbased on a specific type of semantic unit. By interrelating several KGBBs, onecan specify a KGBB-driven FAIREr knowledge graph. Besides implementing semanticunits, the KGBB Framework clearly distinguishes and decouples an internalin-memory data model from data storage, data display, and data access/exportmodels. We argue that this decoupling is essential for solving many problems ofknowledge management systems. We discuss the architecture of the KGBB Frameworkas we envision it, comprising (i) an openly accessible KGBB-Repository fordifferent types of KGBBs, (ii) a KGBB-Engine for managing and operating FAIRErknowledge graphs (including automatic provenance tracking, editing changelog,and versioning of semantic units); (iii) a repository for KGBB-Functions; (iv)a low-code KGBB-Editor with which domain experts can create new KGBBs andspecify their own FAIREr knowledge graph without having to think about semanticmodelling. We conclude with discussing the nine challenges and how the KGBBFramework provides solutions for the issues they raise. While most of what wediscuss here is entirely conceptual, we can point to two prototypes thatdemonstrate the principle feasibility of using semantic units and KGBBs tomanage and structure knowledge graphs.

Knowledge Graph Building Blocks: An easy-to-use Framework for developing FAIREr Knowledge Graphs