Towards Knowledge Graphs Validation through Weighted Knowledge Sources

TL;DR

This paper introduces a Validator that assigns confidence scores to knowledge graph triples by comparing instances across weighted sources, improving validation accuracy and efficiency.

Contribution

It presents a novel approach for KG validation using weighted sources and confidence scoring, enhancing trustworthiness of knowledge bases.

Findings

Achieved at least 75% F-measure in validation accuracy.

Validated 2530 instances in approximately 15 minutes.

Provides insights for improved KG validation architecture.

Abstract

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.