Bookmark Saya

arXiv Open Access 2020

An Industry Evaluation of Embedding-based Entity Alignment

Ziheng Zhang Jiaoyan Chen Xi Chen Hualuo Liu Yuejia Xiang +2 lainnya
Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

cs.CL cs.AI cs.LG

Penulis (7)

Z

Ziheng Zhang

J

Jiaoyan Chen

X

Xi Chen

H

Hualuo Liu

Y

Yuejia Xiang

B

Bo Liu

Y

Yefeng Zheng

Format Sitasi

APA MLA BibTeX
Zhang, Z., Chen, J., Chen, X., Liu, H., Xiang, Y., Liu, B. et al. (2020). An Industry Evaluation of Embedding-based Entity Alignment. https://arxiv.org/abs/2010.11522

Akses Cepat

Lihat di Sumber
Informasi Jurnal
Tahun Terbit
2020
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓