Papers › Differentiating Concepts and Instances for Knowledge Graph Embedding
Differentiating Concepts and Instances for Knowledge Graph Embedding
Xin Lv, Lei Hou, Juanzi Li, Zhiyuan Liu
Concepts, which represent a group of different instances sharing common properties, are essential information in knowledge representation. Most conventional knowledge embedding methods encode both entities (concepts and instances) and relations as vectors in a low dimensional semantic space equally, ignoring the difference between concepts and instances. In this paper, we propose a novel knowledge graph embedding model named TransC by differentiating concepts and instances. Specifically, TransC encodes each concept in knowledge graph as a sphere and each instance as a vector in the same semantic space. We use the relative positions to model the relations between concepts and instances (i.e., instanceOf), and the relations between concepts and sub-concepts (i.e., subClassOf). We evaluate our model on both link prediction and triple classification tasks on the dataset based on YAGO. Experimental results show that TransC outperforms state-of-the-art methods, and captures the semantic transitivity for instanceOf and subClassOf relation. Our codes and datasets can be obtained from https:// github.com/davidlvxin/TransC.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Link Prediction | YAGO39K | TransC (bern) | Hits@1 | 0.298 | #1 of 1 | Archive leaderboard | report |
| Link Prediction | YAGO39K | TransC (bern) | Hits@10 | 0.698 | #1 of 1 | Archive leaderboard | report |
| Link Prediction | YAGO39K | TransC (bern) | Hits@3 | 0.502 | #1 of 1 | Archive leaderboard | report |
| Link Prediction | YAGO39K | TransC (bern) | MRR | 0.42 | #1 of 1 | Archive leaderboard | report |
| Triple Classification | YAGO39K | TransC (bern) | Accuracy | 93.8 | #1 of 1 | Archive leaderboard | report |
| Triple Classification | YAGO39K | TransC (bern) | F1-Score | 93.7 | #1 of 1 | Archive leaderboard | report |
| Triple Classification | YAGO39K | TransC (bern) | Precision | 94.8 | #1 of 1 | Archive leaderboard | report |
| Triple Classification | YAGO39K | TransC (bern) | Recall | 92.7 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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