The Structure and Dynamics of Knowledge Graphs, with Superficiality
This provides a foundational model for researchers and practitioners in AI and data science to better understand and manage knowledge graphs, though it is incremental in building on existing graph theory.
The authors tackled the problem of modeling the complex structure and dynamics of knowledge graphs by introducing the concept of superficiality, which simplifies the representation of relationship overlaps and misdescribed entities, leading to a foundational model for understanding knowledge acquisition and organization.
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. With this model, 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.