Papers by Yongjie Shi
Improving Knowledge Graph Embedding Using Affine Transformations of Entities Corresponding to Each Relation (2021.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding methods use k-dimensional vectors to represent each entity in a knowledge graph. |
| Approach: | They propose to use affine transformations to embed knowledge graphs using previous methods . they propose to add k additional variables to the existing methods to perform embedding . |
| Outcome: | The proposed method outperforms RotatE, Distmult and ComplEx on various data sets. |
Knowledge Graph Embedding by Adaptive Limit Scoring Loss Using Dynamic Weighting Strategy (2022.findings-acl)
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| Challenge: | Existing knowledge graph embedding models use a loss framework to distinguish between correct and incorrect triplets. |
| Approach: | They propose a loss framework that reweights each triplet to highlight the less-optimized triplets. |
| Outcome: | The proposed method performs on several knowledge graph embedding models, including TransE, TransH and ComplEx. |
Learning Hierarchy-Aware Quaternion Knowledge Graph Embeddings with Representing Relations as 3D Rotations (2022.coling-1)
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| Challenge: | Existing knowledge graph embedding models fail to model semantic hierarchies . Existing methods fail to understand the semantic hierarchies of knowledge graphs . |
| Approach: | They propose a model which embeds entities as pure quaternions and constrains the modulus of entities to make them have hierarchical distributions. |
| Outcome: | The proposed model can encode symmetry/antisymmetry, inversion, composition, multiple relation patterns and learn semantic hierarchies simultaneously. |