KGdiff: Tracking the Evolution of Knowledge Graphs

Abstract

A Knowledge Graph (KG) is a machine-readable, labeled graph-like representation of human knowledge. As the main goal of KG is to represent data by enriching it with computer-processable semantics, the knowledge graph creation usually involves acquiring data from external resources and datasets. In many domains, especially in biomedicine, the data sources continuously evolve, and KG engineers and domain experts must not only track the changes in KG entities and their interconnections but introduce changes to the KG schema and the graph population software. We present a framework to track the KG evolution both in terms of the schema and individuals. KGdiff is a software tool that incrementally collects the relevant meta-data information from a KG and compares it to a prior version the KG. The KG is represented in OWL/RDF/RDFS and the meta-data is collected using domain-independent queries. We evaluate our method on different RDF/OWL data sets (ontologies).