Towards Knowledge Graphs Validation through Weighted Knowledge Sources

The performance of applications, such as personal assistants, search engines, and question-answering systems, rely on high-quality knowledge bases, a.k.a. Knowledge Graphs (KGs). To ensure their quality one important task is Knowledge Validation, which measures the degree to which statements or triples of a Knowledge Graph (KG) are correct. KGs inevitably contains incorrect and incomplete statements, which may hinder the adoption of such KGs in business applications as they are not trustworthy. In this paper, we propose and implement a validation approach that computes a confidence score for every triple and instance in a KG. The computed score is based on finding the same instances across different weighted knowledge sources and comparing their features. We evaluated the performance of our Validator by comparing a manually validated result against the output of the Validator. The experimental results showed that compared with the manual validation, our Validator achieved as good precision as the manual validation, although with certain limitations. Furthermore, we give insights and directions toward a better architecture to tackle KG validation.