Summary of Attr-int: a Simple and Effective Entity Alignment Framework For Heterogeneous Knowledge Graphs, by Linyan Yang et al.
Attr-Int: A Simple and Effective Entity Alignment Framework for Heterogeneous Knowledge Graphs
by Linyan Yang, Jingwei Cheng, Chuanhao Xu, Xihao Wang, Jiayi Li, Fu Zhang
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed entity alignment framework, Attr-Int, addresses the challenge of linking entities in heterogeneous knowledge graphs by integrating attribute information interaction methods with any embedding encoder for entity alignment. The framework is evaluated on two new benchmarks that simulate real-world scenarios of heterogeneity and outperforms state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Entity alignment is a task that links entities across different knowledge graphs. Current methods rely on structural isomorphism, but this isn’t always true in real-world situations. This paper tackles the problem by proposing two new benchmarks to test entity alignment methods. The approach also evaluates existing methods and introduces a simple framework called Attr-Int that improves performance. |
Keywords
» Artificial intelligence » Alignment » Embedding » Encoder
