Quality Assessment

Abstract

Independent of the (kinds of) source(s) from which a knowledge graph is created, the resulting initial knowledge graph will usually be incomplete, and will often contain duplicate, contradictory or even incorrect statements, especially when taken from multiple sources. After the initial creation and enrichment of a knowledge graph from external sources, a crucial step is thus to assess the quality of the resulting knowledge graph. By quality, we here refer to fitness for purpose. Quality assessment then helps to ascertain for which purposes a knowledge graph can be reliably used. Take, for instance, the sample of an initial knowledge graph created by the tourist board shown in Figure 7.1. Is this knowledge graph of good quality? Does it exhibit issues that might limit the applications for which it is fit for purpose? Can we define and detect such issues? These questions are crucial to address before the knowledge graph is deployed, but they are also challenging to address in a general way.