Papers with triple classification tasks
Accurate Text-Enhanced Knowledge Graph Representation Learning (N18-1)
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| Challenge: | Existing representation learning methods for knowledge graph representation do not consider the ambiguity of relations and entities. |
| Approach: | They propose a text-enhanced knowledge graph representation learning method which exploits the entity descriptions and triple-specific relation mention to enhance representations. |
| Outcome: | The proposed method outperforms existing representation learning models on link prediction and triple classification tasks and significantly outperformed existing models. |
Differentiating Concepts and Instances for Knowledge Graph Embedding (D18-1)
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| Challenge: | Existing knowledge graph embedding methods encode concepts and instances as vectors in a low-dimensional space, ignoring the difference between concepts and instance. |
| Approach: | They propose a knowledge graph embedding model that separates concepts from instances by differentiating concepts and instances. |
| Outcome: | The proposed model outperforms state-of-the-art methods on link prediction and triple classification tasks on YAGO dataset. |
Self-supervised Quantized Representation for Seamlessly Integrating Knowledge Graphs with Large Language Models (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) are gaining popularity due to their lack of knowledge hallucination and lack of a coherent model. |
| Approach: | They propose a self-supervised quantized representation method to compress KG structural and semantic knowledge into discrete codes that align the format of language sentences. |
| Outcome: | The proposed framework outperforms existing unsupervised methods producing more distinguishable codes on KG link prediction and triple classification tasks. |