The Structure and Dynamics of Knowledge Graphs, with Superficiality

TL;DR

This paper introduces a novel model for understanding the structure and dynamics of large knowledge graphs by incorporating the concept of superficiality, which explains their complex topologies and distribution of facts.

Contribution

It presents the first model that captures the structure and evolution of knowledge graphs through superficiality, enhancing understanding of knowledge organization.

Findings

Superficiality controls overlap between relationships in knowledge graphs.

The model explains the emergence of complex and chaotic topologies.

It predicts the distribution of misdescribed entities in knowledge graphs.

Abstract

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.