Large knowledge graphs combine human knowledge garnered from projects ranging from academia and institutions to enterprises and crowdsourcing. Within such graphs, each relationship between two nodes represents a basic fact involving these two entities. The diversity of the semantics of relationships constitutes the richness of knowledge graphs, leading to the emergence of singular topologies, sometimes chaotic in appearance. However, this complex characteristic can be modeled in a simple way by introducing the concept of superficiality, which controls the overlap between relationships whose facts are generated independently. Superficiality also regulates the balance of the global distribution of knowledge by determining the proportion of misdescribed entities. This is the first model for the structure and dynamics of knowledge graphs. It leads to a better understanding of formal knowledge acquisition and organization.

翻译：大型知识图谱整合了源自学术机构、企业项目及众包等领域的人类知识。此类图谱中，每对节点间的关联关系均代表涉及这两个实体的基本事实。关系语义的多样性构成了知识图谱的丰富性，催生出表面看似混沌的奇异拓扑结构。然而，这种复杂特性可通过引入"浅层性"概念进行简洁建模——该概念控制着事实独立生成的关系间的重叠程度。浅层性还能通过决定错误描述实体的比例来调节知识全局分布的均衡性。这是首个针对知识图谱结构与动力学的理论模型，有助于更深入地理解形式化知识的获取与组织机制。