Facing Changes: Continual Entity Alignment for Growing Knowledge Graphs

Wang, Yuxin; Cui, Yuanning; Liu, Wenqiang; Sun, Zequn; Jiang, Yiqiao; Han, Kexin; Hu, Wei

doi:10.1007/978-3-031-19433-7_12

Cited by 4 publications

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Iterative Geographic Entity Alignment with Cross-Attention

Dsouza,

Yu,

Windoffer

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Aligning schemas and entities of community-created geographic data sources with ontologies and knowledge graphs is a promising research direction for making this data widely accessible and reusable for semantic applications. However, such alignment is challenging due to the substantial differences in entity representations and sparse interlinking across sources, as well as high heterogeneity of schema elements and sparse entity annotations in community-created geographic data. To address these challenges, we propose a novel cross-attention-based iterative alignment approach called IGEA in this paper. IGEA adopts cross-attention to align heterogeneous context representations across geographic data sources and knowledge graphs. Moreover, IGEA employs an iterative approach for schema and entity alignment to overcome annotation and interlinking sparsity. Experiments on real-world datasets from several countries demonstrate that our proposed approach increases entity alignment performance compared to baseline methods by up to 18% points in F1-score. IGEA increases the performance of the entity and tag-to-class alignment by 7 and 8% points in terms of F1-score, respectively, by employing the iterative method.

show abstract