Entity Resolved Knowledge Graphs: A Tutorial

Entity Resolved Knowledge Graphs (ERKG) are a solution to the problem of duplicate nodes in knowledge graphs, which can dilute their power and accuracy. ERKG uses entity resolution to identify and link data records that refer to the same real-world entities, increasing the accuracy and utility of knowledge graphs. In this tutorial, we build an ERKG using Python and Neo4j, integrating Senzing for entity resolution and leveraging Graph Data Science (GDS) library for graph analytics and visualization. We load three datasets about businesses in Las Vegas, run entity resolution, export results, parse JSON, and connect entities with input records. The resulting ERKG is visualized using PyVis, showing clusters of linked records per entity and the convergence of dataset records achieved through entity resolution.