Summary of Unified Interpretation Of Smoothing Methods For Negative Sampling Loss Functions in Knowledge Graph Embedding, by Xincan Feng et al.
Unified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph Embedding
by Xincan Feng, Hidetaka Kamigaito, Katsuhiko Hayashi, Taro Watanabe
First submitted to arxiv on: 5 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper focuses on knowledge graph completion (KGC) through knowledge embedding (KGE), aiming to improve the efficiency of KGE training for large-scale knowledge graphs. The authors recognize the limitations of manual KG creation, emphasizing the importance of automatic methods like KGC. To address the sparsity issue in KGE, they explore smoothing methods for negative sampling loss, including self-adversarial negative sampling (SANS) and subsampling. The paper provides theoretical interpretations of these methods and introduces a new approach, triplet adaptive negative sampling (TANS). Experimental results on various datasets, including FB15k-237, WN18RR, and YAGO3-10, demonstrate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to create and use big collections of information called knowledge graphs. These graphs are important for tasks like natural language processing. One problem with using these graphs is that they can be very big and hard to work with. The authors suggest a new way to make the process more efficient by using a type of training method called negative sampling loss. They also introduce a new approach, triplet adaptive negative sampling (TANS), which can help improve performance. The results from testing this new method show that it works well on different datasets. |
Keywords
* Artificial intelligence * Embedding * Knowledge graph * Natural language processing
