| アイテムタイプ |
会議発表論文 / Conference Paper(1) |
| 公開日 |
2025-08-08 |
| タイトル |
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タイトル |
Unified Interpretation of Smoothing Methods for Negative Sampling Loss Functions in Knowledge Graph Embedding |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ |
conference paper |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Feng, Xincan
上垣外, 英剛
Hayashi, Katsuhiko
渡辺, 太郎
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
Knowledge Graphs (KGs) are fundamental resources in knowledge-intensive tasks in NLP. Due to the limitation of manually creating KGs, KG Completion (KGC) has an important role in automatically completing KGs by scoring their links with KG Embedding (KGE). To handle many entities in training, KGE relies on Negative Sampling (NS) loss that can reduce the computational cost by sampling. Since the appearance frequencies for each link are at most one in KGs, sparsity is an essential and inevitable problem. The NS loss is no exception. As a solution, the NS loss in KGE relies on smoothing methods like Self-Adversarial Negative Sampling (SANS) and subsampling. However, it is uncertain what kind of smoothing method is suitable for this purpose due to the lack of theoretical understanding. This paper provides theoretical interpretations of the smoothing methods for the NS loss in KGE and induces a new NS loss, Triplet Adaptive Negative Sampling (TANS), that can cover the characteristics of the conventional smoothing methods. Experimental results of TransE, DistMult, ComplEx, RotatE, HAKE, and HousE on FB15k-237, WN18RR, and YAGO3-10 datasets and their sparser subsets show the soundness of our interpretation and performance improvement by our TANS. |
| 書誌情報 |
en : Proceedings of the 9th Workshop on Representation Learning for NLP (RepL4NLP-2024)
p. 78-98,
発行日 2024-08-15
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| 会議情報 |
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会議名 |
The 9th Workshop on Representation Learning for NLP (RepL4NLP-2024) |
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開始年 |
2024 |
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開始月 |
08 |
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開始日 |
15 |
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終了年 |
2024 |
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終了月 |
08 |
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終了日 |
15 |
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開催期間 |
2024-08-15 - 2024-08-15 |
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開催地 |
Bangkok, Thailand |
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開催国 |
THA |
| 出版者 |
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出版者 |
Association for Computational Linguistics |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://aclanthology.org/2024.repl4nlp-1.8/ |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
$00A92024 Association for Computational Linguistics. ACL materials are Copyright $00A9 1963$20132025 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License. |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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助成機関名 |
Japan Science and Technology Agency (JST) |
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研究課題番号 |
JPMJSP2140 |
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研究課題名 |
JST SPRING |
