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対照的知識埋め込みと識別的自己重み付けサンプリング

Sheng Wan1, Yibing Zhan2, Shirui Pan3

  • 1College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, 211800, Jiangsu, China.

Neural networks : the official journal of the International Neural Network Society
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まとめ
この要約は機械生成です。

対照学習（CL）は、ネガティブサンプルの重みを適応的に付けることで知識グラフ（KG）埋め込みを強化します。この識別的自己重み付けサンプリング（CoDiSS）フレームワークは、情報量の多いネガティブに焦点を当てることでKG埋め込みモデルを改善します。

キーワード:
グラフ対照学習表現学習自己教師あり学習

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科学分野:

  • 人工知能
  • 機械学習
  • データサイエンス

背景:

  • 知識グラフ（KG）埋め込みは、KGコンポーネントを低次元空間にマッピングします。
  • 既存のKG埋め込みモデルは、スコアリング関数に焦点を当てており、学習フレームワークを無視しています。
  • 対照学習（CL）は、KG埋め込みにおける表現学習の可能性を提供します。

研究 の 目的:

  • 従来のネガティブサンプリングの非効率性に対処するKG埋め込みのための新しいCLフレームワークを導入すること。
  • KG埋め込みモデルの表現力とパフォーマンスを強化すること。

主な方法:

  • 「Contrastive knowledge embedding with Discriminative Self-weighted sampling」（CoDiSS）という柔軟なCLフレームワークを開発しました。
  • 学習への貢献に基づいてネガティブトリプレットの適応的な重み付けメカニズムを実装しました。
  • ネガティブスコア分布を再形成するために、Discriminative Weight Refinement（DWR）損失を導入しました。

主要な成果:

  • CoDiSSは、一様なサンプリングとは異なり、ネガティブトリプレットに重要度を適応的に割り当てます。
  • DWR損失は、情報量の多いネガティブと偽のネガティブを効果的に分離します。
  • CoDiSSは、さまざまなKG埋め込みモデル（TransE、ComplEx、HousE）のパフォーマンスを向上させます。

結論:

  • 提案されたCoDiSSフレームワークは、情報量の多いネガティブから学習し、偽のネガティブを軽減することにより、KG埋め込みモデルを強化します。
  • CoDiSSは、より表現力豊かなKG埋め込みにつながります。
  • このアプローチは、KG埋め込み技術を進歩させるための有望な方向性を提供します。