Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

Knowledge graphs (KGs) have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services. In recent years, researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids. With multiple power grid dispatching knowledge graphs (PDKGs) constructed by different agencies, the knowledge fusion of different PDKGs is useful for providing more accurate decision supports. To achieve this, entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step. Existing entity alignment methods cannot integrate useful structural, attribute, and relational information while calculating entities' similarities and are prone to making many-to-one alignments, thus can hardly achieve the best performance. To address these issues, this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments. This model proposes a novel knowledge graph attention network (KGAT) to learn the embeddings of entities and relations explicitly and calculates entities' similarities by adaptively incorporating the structural, attribute, and relational similarities. Then, we formulate the counterpart assignment task as an integer programming (IP) problem to obtain one-to-one alignments. We not only conduct experiments on a pair of PDKGs but also evaluate our model on three commonly used cross-lingual KGs. Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

I. Introduction

With the rapid deployment of renewable energy and the increase of power electronic equipments, power grids have become increasingly huge, complex, and dynamic cyberphysical systems [1], [2]. Traditional dispatching methods that rely heavily on dispatchers' experience have had difficulty in meeting stability, reliability, and high tolerance requirements. Therefore, there is a need of artificial intelligence and intelligent decision support systems for power grid dispatching. Various machine learning techniques have been applied on diverse problems faced by power systems, such as decisionmaking in dispatching and control [3], the security and privacy protection of power data [4], the automatic maintenance of power equipment [5], and the forecasting and optimization [6], [7] of power flow.