| アイテムタイプ |
会議発表論文 / Conference Paper(1) |
| 公開日 |
2025-07-24 |
| タイトル |
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タイトル |
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion? |
| 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 |
坂井, 優介
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 |
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内容記述 |
Knowledge graphs (KGs) consist of links that describe relationships between entities. Due to the difficulty of manually enumerating all relationships between entities, automatically completing them is essential for KGs. Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities in a KG. Traditional embedding-based KGC methods (e.g. RESCAL, TransE, DistMult, ComplEx, RotatE, HAKE, HousE, etc.) infer missing links using only the knowledge from training data. In contrast, the recent Pre-trained Language Model (PLM)-based KGC utilizes knowledge obtained during pre-training, which means it can estimate missing links between entities by reusing memorized knowledge from pre-training without inference. This part is problematic because building KGC models aims to infer unseen links between entities. However, conventional evaluations in KGC do not consider inference and memorization abilities separately. Thus, a PLM-based KGC method, which achieves high performance in current KGC evaluations, may be ineffective in practical applications. To address this issue, we analyze whether PLM-based KGC methods make inferences or merely access memorized knowledge. For this purpose, we propose a method for constructing synthetic datasets specified in this analysis and conclude that PLMs acquire the inference abilities required for KGC through pre-training, even though the performance improvements mostly come from textual information of entities and relations. |
| 書誌情報 |
en : Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
p. 8091-8106,
ページ数 16,
発行日 2024-06
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| 会議情報 |
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会議名 |
The 2024 Conference of the North American Chapter of the Association for Computational Linguistics |
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開始年 |
2024 |
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開始月 |
06 |
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開始日 |
16 |
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終了年 |
2024 |
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終了月 |
06 |
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終了日 |
21 |
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開催期間 |
2024-06-16 - 2024-06-21 |
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開催地 |
Mexico City, Mexico |
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開催国 |
MEX |
| 出版者 |
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出版者 |
Association for Computational Linguistics |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.18653/v1/2024.naacl-long.447 |
| 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.naacl-long.447/ |
| 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. 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 Society for the Promotion of Science (JSPS) |
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研究課題番号 |
JP23H03458 |
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研究課題名 |
漸進的な知識の拡張を行う汎用自然言語生成モデルの研究 |
