# Towards Knowledge Graphs Validation through Weighted Knowledge Sources

arXiv: https://arxiv.org/abs/2104.12622 · HTML: https://mlsanity.com/paper/2104.12622/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2021.

## Built on by (most cited first, 3 of 3)

- [KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks](https://mlsanity.com/paper/2308.10537/) (2023)
- [Knowledge Graph Curation: A Practical Framework](https://mlsanity.com/paper/2208.08130/) (2022)
- [Evaluation of Pipelines for Data Integration into Knowledge Graphs](https://mlsanity.com/paper/2605.22304/) (2026)

## Abstract (arXiv)

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