Resolving Power Equipment Data Inconsistency via Heterogeneous Network Alignment

Abstract

This paper studies the problem of resolving data inconsistency from multiple sources in managing data related to power equipment for China’s state grid corporation. This paper proposes to automatically align inconsistent devices from multiple sources, i.e., the same devices that have multiple entries with different values in each source, by HENGE, a HEtetrogeneous Network GEneration model. HENGE builds multiple data sources into a heterogeneous graph, and captures complex physical and semantic relationships among devices. HENGE combines both feature and relational information and improves alignment accuracy by feature-enhanced residual graph encoder and disentangled representation learning. HENGE is capable to learn from a small amount of labeled data, through a uniformity autoencoder trained on an unsupervised generation task. Experiments on two real-world datasets demonstrate the capability of HENGE in resolving inconsistent device entries in multiple sources.