Collective Multi-type Entity Alignment Between Knowledge Graphs

Qi Zhu, Hao Wei, Bunyamin Sisman, Da Zheng, Christos Faloutsos, Xin Luna Dong, Jiawei Han

Abstract:

Knowledge graph (e.g. Freebase, YAGO) is a multi-relational graph
representing rich factual information among entities of various
types. Entity alignment is the key step towards knowledge graph
integration from multiple sources. It aims to identify entities across
different knowledge graphs that refer to the same real world entity.
However, current entity alignment systems overlook the sparsity
of different knowledge graphs and can not align multi-type entities by one single model. In this paper, we present a Collective
Graph neural network for Multi-type entity Alignment, called CGMuAlign. Different from previous work, CG-MuAlign jointly aligns
multiple types of entities, collectively leverages the neighborhood
information and generalizes to unlabeled entity types. Specifically,
we propose novel collective aggregation function tailored for this
task, that (1) relieves the incompleteness of knowledge graphs via
both cross-graph and self attentions, (2) scales up efficiently with
mini-batch training paradigm and effective neighborhood sampling
strategy. We conduct experiments on real world knowledge graphs
with millions of entities and observe the superior performance
beyond existing methods. In addition, the running time of our approach is much less than the current state-of-the-art deep learning
methods.