科学的知識のギャップと再現性:起源,断言,証拠オントロジーの調査
Tek Raj Chhetri1, Yaroslav O Halchenko2, Dorota Jarecka1
1McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
まとめ
科学的な本体論と知識グラフ (KG) は,再現性を向上させるためにデータを構造化します. このレビューでは,主張,証拠,起源に関する23の本体論を検証し,科学的な進歩のための課題と機会を特定しています.
科学分野:
- バイオメディカル・インフォマティック
- データサイエンス
- 科学出版
背景:
- 科学的な出版物の急速な増加と 複雑な実験デザインは 時代遅れの課題です
- 構造化されていない科学的主張と限られた起源は研究の再現性を妨げます.
- 断言,証拠,出所を把握するための構造化されたデータ表現の必要性.
研究 の 目的:
- 科学的主張,証拠,起源を把握するための既存の本体論をレビューする.
- 関連するオントロジーの現在の風景の概要を提供すること.
- この分野における重要な課題と機会を特定する.
主な方法:
- 科学文献の体系的なレビュー
- 23のオントロジーを特定し分析した.
- オントロジーの分類は,主張/証拠 (13) と由来 (10) に焦点を当てたもの.
主要な成果:
- 23のオントロジーが特定され,分析されました.
- 13のオントロジーは主張と証拠に焦点を当てている.
- 10のオントロジーは 起源に焦点を当てています
結論:
- オントロジーと知識グラフ (KG) は科学的データの構造化に不可欠です.
- 現在のオントロジーにおける課題に対処することで 再現性を向上させることができます
- 科学的進歩のためにKGを完全に活用するには,さらなる開発が必要です.
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