Towards Understanding the Geometry of Knowledge Graph Embeddings理解
Chandrahas et al. 2018 ACL. 論文背景 知識圖譜的embedding方式有多種，可按得分函數(score function)的計算表達式分爲「加法模型」和「乘法模型」兩類（見下圖1）。本文提出新的度量指標，研究各種embedding方式的幾何(geometry)性質。 圖1：知識圖譜embedding得分函數分類 度量指標 ATM(alignment to mean):
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相關文章
- 1. 論文理解—— DisenE: Disentangling Knowledge Graph Embeddings
- 2. KBGAN: Adversarial Learning for Knowledge Graph Embeddings理解
- 3. Enriching BERT with Knowledge Graph Embeddings for Document Classification
- 4. Convolutional 2D Knowledge Graph Embeddings 閱讀筆記
- 5. Towards Understanding Google Titan
- 6. 【論文閱讀】Deep Reasoning with Knowledge Graph for Social Relationship Understanding
- 7. EMNLP2019 | Knowledge-Aware Graph Networks
- 8. Applications of Graph Neural Networks
- 9. 論文解讀：Connecting Embeddings for Knowledge Graph Entity Typing
- 10. 閱讀分享：Enriching BERT with Knowledge Graph Embeddings for Document Classification