An Industry Evaluation of Embedding-based Entity Alignment
Ziheng Zhang, Hualuo Liu, Jiaoyan Chen, Xi Chen, Bo Liu, YueJia Xiang, Yefeng Zheng
Abstract
Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of unbiased seed mappings for training and validation, which significantly limits their usage. In this study, we evaluate those state-of-the-art methods in an industrial context, where the impact of seed mappings with different sizes and different biases is explored. Besides the popular benchmarks from DBpedia and Wikidata, we contribute and evaluate a new industrial benchmark that is extracted from two heterogeneous knowledge graphs (KGs) under deployment for medical applications. The experimental results enable the analysis of the advantages and disadvantages of these alignment methods and the further discussion of suitable strategies for their industrial deployment.
BibTeX
@inproceedings{zhang-etal-2020-industry,
title = "An Industry Evaluation of Embedding-based Entity Alignment",
author = "Zhang, Ziheng and
Liu, Hualuo and
Chen, Jiaoyan and
Chen, Xi and
Liu, Bo and
Xiang, YueJia and
Zheng, Yefeng",
editor = "Clifton, Ann and
Napoles, Courtney",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
month = dec,
year = "2020",
address = "Online",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-industry.17/",
doi = "10.18653/v1/2020.coling-industry.17",
pages = "179--189"
}