SEA: A Scalable Entity Alignment System

Junyang Wu, Tianyi Li*, Lu Chen, Yunjun Gao, Zhiheng Wei

*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2 Citations (Scopus)
73 Downloads (Pure)

Abstract

Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). State-of-the-art EA approaches generally use Graph Neural Networks (GNNs) to encode entities. However, most of them train the models and evaluate the results in a full-batch fashion, which prohibits EA from being scalable on large-scale datasets. To enhance the usability of GNN-based EA models in real-world applications, we present SEA, a scalable entity alignment system that enables users to (i) train large-scale GNNs for EA, (ii) speed up the normalization and the evaluation process, and (iii) report clear results for users to estimate different models and parameter settings. SEA can be run on a computer with merely one graphic card. Moreover, SEA encompasses six state-of-the-art EA models and provides access for users to quickly establish and evaluate their own models. Thus, SEA allows users to perform EA without being involved in tedious implementations, such as negative sampling and GPU-accelerated evaluation. With SEA, users can gain a clear view of the model performance. In the demonstration, we show that SEA is user-friendly and is of high scalability even on computers with limited computational resources.

Original languageEnglish
Title of host publicationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23)
Number of pages5
PublisherAssociation for Computing Machinery
Publication date19 Jul 2023
Pages3175-3179
ISBN (Electronic)978-1-4503-9408-6
DOIs
Publication statusPublished - 19 Jul 2023
EventThe 46th International ACM SIGIR Conference on Research and Development in Information Retrieval - Tapei, Taiwan, Province of China
Duration: 23 Jul 202327 Jul 2023

Conference

ConferenceThe 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
Country/TerritoryTaiwan, Province of China
CityTapei
Period23/07/202327/07/2023

Keywords

  • Entity Alignment, Knowledge Graphs

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