id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-020814-1ty7wzlv	Berrendorf, Max	Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned	2020-03-24		.txt	text/plain	2314	144	55	In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task. Graph Convolutional Networks (GCN) [7, 9] , which have been recently become increasingly popular, are at the core of state-of-the-art methods for entity alignments in KGs [3, 6, 22, 24, 27] . 1. We investigate the reproducibility of the published results of a recent GCNbased method for entity alignment and uncover differences between the method's description in the paper and the authors' implementation. Overview of used datasets with their sizes in the number of triples (edges), entities (nodes), relations (different edge types) and alignments. GCN-Align [22] is a GCN-based approach to embed all entities from both graphs into a common embedding space. Semi-supervised entity alignment via knowledge graph embedding with awareness of degree difference Entity alignment between knowledge graphs using attribute embeddings	./cache/cord-020814-1ty7wzlv.txt	./txt/cord-020814-1ty7wzlv.txt