Towards Knowledge Graphs Validation Through Weighted Knowledge Sources

The performance of applications, such as personal assistants and search engines, relies on high-quality knowledge bases, a.k.a. Knowledge Graphs (KGs). To ensure their quality one important task is validation, which measures the degree to statements or triples KGs are semantically correct. inevitably contain incorrect incomplete statements, may hinder adoption in business applications they not trustworthy. In this paper, we propose implement a Validator that computes confidence score for every triple instance KGs. computed based finding same instances across different weighted sources comparing features. We evaluate our approach by its results against baseline validation. Our suggest can validate with an f-measure at least 75%. Time-wise, Validator, performed validation 2530 15 minutes approximately. Furthermore, give insights directions toward better architecture tackle KG