Informed Multi-context Entity Alignment

Abstract

Entity alignment is a crucial step in integrating knowledge graphs (KGs) from multiple sources. Previous attempts at entity alignment have explored different KG structures, such as neighborhood-based and path-based contexts, to learn entity embeddings, but they are limited in capturing the multi-context features. Moreover, most approaches directly utilize the embedding similarity to determine entity alignment without considering the global interaction among entities and relations. In this work, we propose an Informed Multi-context Entity Alignment (IMEA) model to address these issues. In particular, we introduce Transformer to flexibly capture the relation, path, and neighborhood contexts, and design holistic reasoning to estimate alignment probabilities based on both embedding similarity and the relation/entity functionality. The alignment evidence obtained from holistic reasoning is further injected back into the Transformer via the proposed soft label editing to inform embedding learning. Experimental results on several benchmark datasets demonstrate the superiority of our IMEA model compared with existing state-of-the-art entity alignment methods.

arXiv commentaccepted by wsdm 2022

References in corpus (5)

• Adam: A Method for Stochastic Optimization
• A Benchmarking Study of Embedding-based Entity Alignment for Knowledge Graphs
• Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs
• Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Mining
• PARIS: Probabilistic Alignment of Relations, Instances, and Schema

Cited by in corpus (1)

• Large-scale Entity Alignment via Knowledge Graph Merging, Partitioning and Embedding