Degree-Aware Alignment for Entities in Tail

• URL: http://arxiv.org/abs/2005.12132v1
• Date: Mon, 25 May 2020 14:15:49 GMT
• Title: Degree-Aware Alignment for Entities in Tail
• Authors: Weixin Zeng, Xiang Zhao, Wei Wang, Jiuyang Tang, and Zhen Tan
• Abstract summary: We propose a novel framework for entity alignment (EA) We identify entity's degree as important guidance to effectively fuse two different sources of information. For post-alignment, we propose to complement original KGs with facts from their counterparts by using confident EA results as anchors.
• Score: 11.153455121529236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge. Existing EA solutions mainly rely on structural information to align entities, typically through KG embedding. Nonetheless, in real-life KGs, only a few entities are densely connected to others, and the rest majority possess rather sparse neighborhood structure. We refer to the latter as long-tail entities, and observe that such phenomenon arguably limits the use of structural information for EA. To mitigate the issue, we revisit and investigate into the conventional EA pipeline in pursuit of elegant performance. For pre-alignment, we propose to amplify long-tail entities, which are of relatively weak structural information, with entity name information that is generally available (but overlooked) in the form of concatenated power mean word embeddings. For alignment, under a novel complementary framework of consolidating structural and name signals, we identify entity's degree as important guidance to effectively fuse two different sources of information. To this end, a degree-aware co-attention network is conceived, which dynamically adjusts the significance of features in a degree-aware manner. For post-alignment, we propose to complement original KGs with facts from their counterparts by using confident EA results as anchors via iterative training. Comprehensive experimental evaluations validate the superiority of our proposed techniques.

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