Toward Entity Alignment in the Open World: An Unsupervised Approach with Confidence Modeling

Xiang Zhao,Xinyi Li,Minnan Luo,Weixin Zeng,Jiuyang Tang,Qinghua Zheng

doi:10.1007/s41019-022-00178-4

Xiang Zhao, Xinyi Li + Show 4 more

Open Access

https://doi.org/10.1007/s41019-022-00178-4

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Abstract

Entity alignment (EA) aims to discover the equivalent entities in different knowledge graphs (KGs). It is a pivotal step for integrating KGs to increase knowledge coverage and quality. Recent years have witnessed a rapid increase of EA frameworks. However, state-of-the-art solutions tend to rely on labeled data for model training. Additionally, they work under the closed-domain setting and cannot deal with entities that are unmatchable. To address these deficiencies, we offer an unsupervised framework UEA that performs entity alignment in the open world. Specifically, we first mine useful features from the side information of KGs. Then, we devise an unmatchable entity prediction module to filter out unmatchable entities and produce preliminary alignment results. These preliminary results are regarded as the pseudo-labeled data and forwarded to the progressive learning framework to generate structural representations, which are integrated with the side information to provide a more comprehensive view for alignment. Finally, the progressive learning framework gradually improves the quality of structural embeddings and enhances the alignment performance. Furthermore, noticing that the pseudo-labeled data are of various qualities, we introduce the concept of confidence to measure the probability of an entity pair of being true and develop a confidence-based unsupervised EA framework CUEA. Our solutions do not require labeled data and can effectively filter out unmatchable entities. Comprehensive experimental evaluations validate the superiority of our proposals .

Highlights

Knowledge graphs (KGs) have been applied to various fields such as natural language processing and information retrieval
– We extend using our progressive learning framework (UEA) to a confidence-based framework CUEA, where we put forward C-thresholded bidirectional nearest neighbor search (TBNNS) to assign confidence scores to aligned entity pairs and incorporate such probabilities into the KG representation learning process, so as to improve the quality of learned entity representations and the alignment performance
We forward the textual distance matrix generated by using the side information to the unmatchable entity prediction module to produce the preliminary alignment results, which are considered as pseudo-labeled data for learning unified KG embeddings

Summary

Introduction

Knowledge graphs (KGs) have been applied to various fields such as natural language processing and information retrieval. To improve the quality of KGs, many efforts have been dedicated to the alignment of KGs, since different KGs usually contain complementary information. Entity alignment (EA), which aims to identify equivalent entities in different KGs, is a crucial step of KG alignment and has been intensively studied over the last few years [1,2,3,4,5,6,7,8]. We use Example 1 to illustrate this task. Example 1 In Figure 1 are a partial English KG and a partial Spanish KG concerning the director Hirokazu Koreeda, where the dashed lines indicate known alignments (i.e., seeds). The task of EA aims to identify equivalent entity pairs between two KGs, e.g., (Shoplifters, Manbiki Kazoku)

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