Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data
This work addresses the challenge of incomplete knowledge graphs for AI applications by improving completion accuracy despite noisy data, representing an incremental advancement in domain-specific methods.
The authors tackled the problem of knowledge graph completion using noisy multi-sourced data by proposing a method that integrates graph neural networks, value alignment, and truth inference, achieving superior accuracy in completing missing facts and discovering new ones compared to state-of-the-art methods.
Knowledge graphs (KGs) have become a valuable asset for many AI applications. Although some KGs contain plenty of facts, they are widely acknowledged as incomplete. To address this issue, many KG completion methods are proposed. Among them, open KG completion methods leverage the Web to find missing facts. However, noisy data collected from diverse sources may damage the completion accuracy. In this paper, we propose a new trustworthy method that exploits facts for a KG based on multi-sourced noisy data and existing facts in the KG. Specifically, we introduce a graph neural network with a holistic scoring function to judge the plausibility of facts with various value types. We design value alignment networks to resolve the heterogeneity between values and map them to entities even outside the KG. Furthermore, we present a truth inference model that incorporates data source qualities into the fact scoring function, and design a semi-supervised learning way to infer the truths from heterogeneous values. We conduct extensive experiments to compare our method with the state-of-the-arts. The results show that our method achieves superior accuracy not only in completing missing facts but also in discovering new facts.