Knowledge graphs and ontologies provide promising technical solutions for implementing the FAIR Principles for Findable, Accessible, Interoperable, and Reusable data and metadata. However, they also come with their own challenges. Nine such challenges are discussed and associated with the criterion of cognitive interoperability and specific FAIREr principles (FAIR + Explorability raised) that they fail to meet. We introduce an easy-to-use, open source knowledge graph framework that is based on knowledge graph building blocks (KGBBs). KGBBs are small information modules for knowledge-processing, each based on a specific type of semantic unit. By interrelating several KGBBs, one can specify a KGBB-driven FAIREr knowledge graph. Besides implementing semantic units, the KGBB Framework clearly distinguishes and decouples an internal in-memory data model from data storage, data display, and data access/export models. We argue that this decoupling is essential for solving many problems of knowledge management systems. We discuss the architecture of the KGBB Framework as we envision it, comprising (i) an openly accessible KGBB-Repository for different types of KGBBs, (ii) a KGBB-Engine for managing and operating FAIREr knowledge 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 and specify their own FAIREr knowledge graph without having to think about semantic modelling. We conclude with discussing the nine challenges and how the KGBB Framework provides solutions for the issues they raise. While most of what we discuss here is entirely conceptual, we can point to two prototypes that demonstrate the principle feasibility of using semantic units and KGBBs to manage and structure knowledge graphs.

翻译：知识图谱与本体为实施FAIR原则（可发现、可访问、可互操作、可复用）的数据与元数据提供了有前景的技术方案，但同时也面临自身挑战。本文探讨了九类具体挑战，这些挑战均与认知互操作性准则及特定FAIREr原则（FAIR + 可探索性提升）相关联，且未满足这些原则。我们介绍了一种基于知识图谱构建模块（KGBBs）的易用型开源知识图谱框架。KGBBs是用于知识处理的小型信息模块，每个模块基于特定类型的语义单元。通过关联多个KGBB，可构建由KGBB驱动的FAIREr知识图谱。除实现语义单元外，KGBB框架明确区分并解耦了内部内存数据模型与数据存储、数据展示及数据访问/导出模型。我们认为这种解耦是解决知识管理系统诸多问题的关键。本文讨论了我们设想的KGBB框架架构，包括：(i) 面向不同类型KGBB的开放访问KGBB资源库；(ii) 用于管理和运行FAIREr知识图谱的KGBB引擎（含自动溯源追踪、编辑变更日志及语义单元版本控制）；(iii) KGBB函数资源库；(iv) 低代码KGBB编辑器，领域专家可借此创建新KGBB并自主定义FAIREr知识图谱，无需考虑语义建模。最后，我们再次审视九类挑战，探讨KGBB框架如何为这些问题提供解决方案。尽管本文讨论内容多为概念性设计，但已开发两个原型系统，验证了利用语义单元和KGBB管理、结构化知识图谱的可行性原则。