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Ligand Binding and Linkage00:49

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
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When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
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显式和隐式特征对比学习模型用于知识图链接预测.

Xu Yuan1,2, Weihe Wang1,2, Buyun Gao1,2

  • 1School of Software Technology, Dalian University of Technology, Dalian 116024, China.

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概括
此摘要是机器生成的。

这项研究引入了一种新的方法,通过将隐含的语义特征与明确的结构信息相结合,用于知识图链接预测. 该方法增强了实体表示,提高了知识图中预测关系的准确性.

关键词:
相反的学习学习学习.隐含的语义特征是一种隐含的语义特征.知识图表知识图表链接预测 链接预测

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科学领域:

  • 人工智能的人工智能
  • 数据科学数据科学数据科学
  • 机器学习 机器学习

背景情况:

  • 知识图链接预测对于推断实体之间的关系至关重要.
  • 图形神经网络和对比学习显示出希望,但忽视了隐含实体关联.
  • 现有的方法与遥远但含义丰富的实体和受约束的对比学习作斗争.

研究的目的:

  • 为了解决当前知识图形链接预测模型的局限性.
  • 为了捕捉实体之间超越直接联系的隐含关联.
  • 通过结合隐式和显式特征来改进实体表示.

主要方法:

  • 开发了一个隐性特征提取模块,使用潜向量空间聚类.
  • 集成了一个子图机制,以保存明确的结构信息.
  • 组合隐含的语义和明确的结构特征,用于自我监督的信号.

主要成果:

  • 拟议的模型有效地通过挖掘概念级语义特征来丰富实体表示.
  • 子图机制保留了明确连接的实体的关键结构信息.
  • 对基准数据集的实验结果表明,相对于最先进的基线,性能优越.

结论:

  • 这种新的方法成功地整合了隐式和显式特征,用于增强知识图的链接预测.
  • 该方法捕获了遥远的,含义丰富的实体,以前被其他模型忽视了.
  • 这项工作推进了知识图表构建和推理领域.