Summary of Aligning Multiple Knowledge Graphs in a Single Pass, by Yaming Yang et al.
Aligning Multiple Knowledge Graphs in a Single Pass
by Yaming Yang, Zhe Wang, Ziyu Guan, Wei Zhao, Weigang Lu, Xinyan Huang, Jiangtao Cui, Xiaofei He
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes a novel framework called MultiEA for entity alignment (EA) across multiple knowledge graphs (KGs). The authors focus on aligning more than two KGs, filling a research gap in existing EA methods. They first embed entities from all candidate KGs into a common feature space using a shared KG encoder. Then, they explore three alignment strategies to minimize distances among pre-aligned entities and propose an innovative inference enhancement technique to improve performance by incorporating high-order similarities. The framework is evaluated on two new real-world benchmark datasets through extensive experiments, showing that MultiEA can effectively and efficiently align multiple KGs in a single pass. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fuse different knowledge graphs together by finding equivalent entities across them. Before, most methods only worked with two graphs, but this one tackles more than two. The authors propose a new way to do this called MultiEA. First, they use a special encoder to put all the entities from each graph into the same space. Then, they try three different ways to match up the similar entities and add an extra trick to make it even better. They test their method on real-world datasets and show that it can do the job quickly and accurately. |
Keywords
» Artificial intelligence » Alignment » Encoder » Inference
