Papers by Linan Yue
RHGN: Relation-gated Heterogeneous Graph Network for Entity Alignment in Knowledge Graphs (2023.findings-acl)
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| Challenge: | Existing methods for entity alignment fail to account for heterogeneity among KGs and distinction between KG entities and relations. |
| Approach: | They propose a Relation-gated Heterogeneous Graph Network (RHGN) that uses a relation-gate based convolutional layer to distinguish relations and entities in the KG. |
| Outcome: | Extensive experiments on four datasets show that the proposed method is superior to state-of-the-art methods. |
Interventional Rationalization (2023.emnlp-main)
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| Challenge: | Existing methods for rationalization use spurious correlations in data to compose rationales and make predictions. |
| Approach: | They propose a method to discover the causal rationales by using a structural causal model. |
| Outcome: | The proposed method is based on the causal theory and validates on three real-world datasets. |