KBQ: a tool for Knowledge Base Quality assessment using evolution analysis

Abstract

Knowledge bases are becoming essential components for tasks that require automation with some degrees of intelligence. It is crucial to establish automatic and timely checks to ensure highlevel quality of the knowledge base content (i.e., entities, types, and relations). In this paper, we present KBQ, a tool that automates the detection and report generation of quality issues for evolving knowledge bases. KBQ analyzes the evolution of a KB by measuring the frequency of change, the change pattern, the change impact and the causes of changes of resources and properties. Data collection and profiling tasks are performed using Loupe, an online tool for linked data profiling. We describe KBQ in action on two different use cases, and we report the benefits that it introduced. KBQ is published as open source project, and a demo is available at http: //datascience.ismb.it/shiny/KBQ/