テキストからセマンティック・フィーチャー・リレーションの抽出をグループ化して,医療用植物に関する知識をソーシャルメディアで表現する
Chaveevan Pechsiri1, Intaka Piriyakul2, Joseph Santhi Pechsiri3
1College of Innovative Technology and Engineering, Dhurakij Pundit University, Bangkok, Thailand.
Frontiers in artificial intelligence
|August 27, 2025
まとめ
この研究は,タイの医学書から PlantPart-MedicinalPropertyGroup の関係を取り出し,代替療法に関する知識グラフを作成しています. この方法は,特にCOVID-19のような健康危機の際に,使用者がハーブ特性を理解するのに役立ちます.
科学分野:
- 自然言語処理
- バイオ情報学
- エスノボタニー
背景:
- 薬用植物に関する知識は 代替治療に不可欠です 特にパンデミックの間は
- タイの医学的文章は,しばしば基本的語彙単位 (EDU) で動詞のフレーズを通して特性を表現します.
- これらのテキストからPlantPart-MedicinalPropertyGroupの関係を抽出することは,複雑な言語と注釈の欠如のために課題を提示します.
研究 の 目的:
- 薬局の学術ウェブサイトからPlantPart-MedicinalPropertyGroupの関係を抽出する方法を開発する.
- 薬用植物に関する知識のグラフィカルな表現を 作成する.
- 地元ユーザーや患者が代替医療情報にアクセスできるようにする
主な方法:
- 薬効性概念EDUを特定するために HerbMedからSolving-Verbコンセプトセットを使用しました.
- 薬効性概念表 (MPC) を作成するために,言葉の同時出現 (word-co) パターンを適用した.
- 関係抽出のための文字列マッチングと構造方程式モデリングを使用しています.
主要な成果:
- PlantPart-MedicinalPropertyGroupとの関係を高品質に 引き出すことに成功しました
- 薬用植物に関する知識をグラフィックに表現する方法を開発した.
- 明確な関係クラスラベリングなしで自動化された関係抽出の実現可能性を実証した.
結論:
- 提案されたアプローチは 薬用植物に関する貴重な知識を 効果的に抽出します
- この方法は代替療法に関する情報のアクセシビリティを高めることができます.
- グラフィカルな表現は,複雑な薬用植物特性を理解するのに役立ちます.
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