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Ogive Graph01:07

Ogive Graph

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Relationship Formation02:12

Relationship Formation

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What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Drug Dependence01:17

Drug Dependence

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Medications are typically administered to achieve therapeutic effects. Some drugs can modify an individual's mood and perception, frequently resulting in various enjoyable experiences. However, this can result in drug dependency, a condition marked by continuous drug use despite potential negative consequences. Drug dependency primarily falls into two categories: psychological and physical dependence. Psychological dependence occurs when the pleasurable feelings induced by the drug...
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

155
An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
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Updated: Jan 7, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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依存関係解析とグラフニューラルネットワークに基づくエンティティ関係抽出方法

Fupeng Wei1, Xing Liu2, Limin Pan3

  • 1School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.

Scientific reports
|December 30, 2025
PubMed
まとめ

MGRelモデルは、構文解析とグラフニューラルネットワークを統合することで、キャンパスセキュリティにおけるエンティティ関係抽出を改善します。これにより、精度が向上し、複雑な関係を処理できるようになり、交通安全のための知識獲得が促進されます。

背景:

  • 現在の知識グラフの3項抽出方法は、キャンパスセキュリティデータにおける意味の曖昧さ、関係の重複、エンティティ間の弱い関連性に対処する際に苦労しており、抽出精度と再現率を低下させています。
  • 既存の技術は、キャンパス交通安全管理に不可欠な、複雑で重複する関係を管理するための柔軟性に欠けています。

結論:

  • MGRelモデルは、エンティティ関係抽出において既存の技術を大幅に上回る性能を示しました。
  • このモデルは、特にキャンパス交通安全において、キャンパスセキュリティアプリケーションにかなりの利点と潜在的な価値を示しています。
キーワード:
依存関係解析エンティティ関係抽出グラフニューラルネットワークスマートキャンパスセキュリティトリプル分類

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  • 構文解析とGNNの統合は、複雑な関係抽出の課題に対する堅牢なソリューションを提供します。