Work

High-quality Task Division for Large-scale Entity Alignment

Year: 2022

Type: conference-paper

Abstract: Entity Alignment (EA) aims to match equivalent entities that refer to the same real-world objects and is a key step for Knowledge Graph (KG) fusion. Most neural EA models cannot be applied to large-scale real-life KGs due to their excessive consumption of GPU memory and time. One promising solution is to divide a large EA task into several subtasks such that each subtask only needs to match two small subgraphs of the original KGs. However, it is challenging to divide the EA task without losing effectiveness. Existing methods display low coverage of potential mappings, insufficient evidence in context graphs, and largely differing subtask sizes.

Source: Proceedings of the ... ACM International Conference on Information & Knowledge Management

DOI: 10.1145/3511808.3557352

Authors: Bing Liu, Wen Hua, Guido Zuccon, Genghong Zhao, Xia Zhang

Institutions: The University of Queensland, Neusoft (China)

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Australian Research Council, National Key Research and Development Program of China

Awards: G5697338938, G8115727103