Triple Trustworthiness Measurement for Knowledge Graph

TL;DR

This paper proposes a neural network-based model to measure the trustworthiness of triples in knowledge graphs, improving error detection by combining semantic and global inference information.

Contribution

It introduces a novel crisscrossing neural network model that assesses triple trustworthiness at multiple levels, enhancing error detection in knowledge graphs.

Findings

Significant improvement over existing models in error detection accuracy.

Effective fusion of semantic and global inference information.

Validated on real-world dataset FB15K.

Abstract

The Knowledge graph (KG) uses the triples to describe the facts in the real world. It has been widely used in intelligent analysis and applications. However, possible noises and conflicts are inevitably introduced in the process of constructing. And the KG based tasks or applications assume that the knowledge in the KG is completely correct and inevitably bring about potential deviations. In this paper, we establish a knowledge graph triple trustworthiness measurement model that quantify their semantic correctness and the true degree of the facts expressed. The model is a crisscrossing neural network structure. It synthesizes the internal semantic information in the triples and the global inference information of the KG to achieve the trustworthiness measurement and fusion in the three levels of entity level, relationship level, and KG global level. We analyzed the validity of the model output confidence values, and conducted experiments in the real-world dataset FB15K (from Freebase) for the knowledge graph error detection task. The experimental results showed that compared with other models, our model achieved significant and consistent improvements.