Research map / Graph learning / Knowledge and temporal graphs
Learning Triple Embeddings from Knowledge Graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| encode path triples with Triple2Vec triple embeddings instead of entity+type+predicate concatenation | KPRN · MovieLens-1M (MI) | hit@5: 0.676 -> 0.685 | Helped |
| encode path triples with Triple2Vec embeddings | KPRN · MovieLens-1M (MI) | ndcg@15: 0.632 -> 0.654 | Helped |
| encode path triples with Triple2Vec embeddings | KPRN · KKBox | hit@5: 0.717 -> 0.723 | Helped |
| encode path triples with Triple2Vec embeddings | KPRN · KKBox | ndcg@15: 0.652 -> 0.685 | Helped |
| direct triple embeddings (Triple2Vec) vs averaging node embeddings or concatenating ConvE/RotatE embeddings | triple classification · DBLP, Yago, Foursquare | Micro/Macro F1 | Helped |
| use metapath2vec instead of node2vec/DeepWalk for triple embedding by averaging | baseline · triple classification | Micro/Macro F1 | Hurt |
Closest papers by meaning
- Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction (AAAI 2023 · similarity 0.91)
- Improved Knowledge Graph Embedding Using Background Taxonomic Information (AAAI 2019 · similarity 0.90)
- Knowledge Graph Embedding With Iterative Guidance From Soft Rules (AAAI 2018 · similarity 0.89)
- Towards Continual Knowledge Graph Embedding via Incremental Distillation (AAAI 2024 · similarity 0.87)
- Clustering then Propagation: Select Better Anchors for Knowledge Graph Embedding (NeurIPS 2024 · similarity 0.87)
- Knowledge Graphs Can be Learned with Just Intersection Features (ICML 2024 · similarity 0.87)
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