Papers by C.-C. Jay Kuo
GreenKGC: A Lightweight Knowledge Graph Completion Method (2023.acl-long)
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| Challenge: | Knowledge graph completion (KGC) aims to discover missing relationships in knowledge graphs (KGs). |
| Approach: | They propose a modularized knowledge graph completion solution that learns embeddings for entities and relations through a score function. |
| Outcome: | Experimental results show that GreenKGC outperforms SOTA methods in low dimensions and even better against high-dimensional models with a much smaller model size. |
Just Rank: Rethinking Evaluation with Word and Sentence Similarities (2022.acl-long)
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| Challenge: | Word and sentence similarity tasks are the de facto evaluation method for embeddings. |
| Approach: | They propose a new intrinsic evaluation method called EvalRank which shows a much stronger correlation with downstream tasks. |
| Outcome: | The proposed method shows a much stronger correlation with downstream tasks and is released for future benchmarking purposes. |
Compounding Geometric Operations for Knowledge Graph Completion (2023.acl-long)
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| Challenge: | Knowledge graph embedding (KGE) is one of the most fundamental problems in AI research. |
| Approach: | They propose a new knowledge graph embedding model by leveraging translation, rotation, and scaling operations to form a composite one. |
| Outcome: | The proposed model outperforms existing models on three KG prediction tasks. |