オントロジーを拡張するためのボックスエンブレディング:データ主導で解釈可能なアプローチ
Adel Memariani1, Martin Glauer2, Simon Flügel3
1Data Science Group (DICE), Heinz Nixdorf Institute, Paderborn University, Warburger Str. 100, 33098, Paderborn, North Rhine-Westphalia, Germany. adel.memariani@uni-paderborn.de.
Journal of cheminformatics
|September 2, 2025
まとめ
この研究は,階層的な関係を表すための箱状の埋め込みを使用して,マルチラベル分類で解釈可能なディープラーニングのための新しい方法を導入しています. このアプローチは,オントロジカルな概念化との一貫性を確保しながら,最先端のパフォーマンスを達成します.
科学分野:
- 人工知能
- 化学情報学
- バイオ情報学
背景:
- ディープラーニングモデルは透明性がなく 象徴的な知識の抽出を妨げています
- 複雑なモデルの出力を理解するには 解釈可能なAIが不可欠です
- マルチラベル分類のタスクには,しばしば固有の階層的なラベル構造が含まれます.
研究 の 目的:
- ディープラーニングモデルからシンボリックな知識を導き出す方法を開発する.
- モデルアウトプットに分類的構造を適用し,解釈性を向上させる.
- 幾何学的な埋め込みを使用して,マルチラベルデータセットで暗黙の論理的関係を表現する.
主な方法:
- ベクトル空間におけるオントロジークラスの 箱状の埋め込みを使用した.
- 訓練中にモデルアウトプットに分類構造を強制した.
- ChEBIオントロジーのサブクラス関係を近似することによってモデルのパフォーマンスを評価します.
主要な成果:
- このモデルは,ラベル間の暗黙の階層的な関係をうまく捉えています.
- オントロジカル・コンセプチュアライゼーションと一貫性を確保した.
- マルチラベル分類のタスクで最先端の性能を達成しました.
結論:
- 提案されたアプローチは,化学分類で解釈可能な出力を可能にします.
- 分子の幾何学的な表現は論理的な関係を理解するのに役立ちます.
- 暗黙の階層は,訓練中に明示的な分類法なしで学習されます.
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