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相关实验视频

Updated: Jul 24, 2025

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

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社会世界的知识:建模和应用.

Nir Lotan1, Einat Minkov1

  • 1Information Systems Department, University of Haifa, Haifa, Israel.

PloS one
|July 7, 2023
PubMed
概括
此摘要是机器生成的。

我们介绍SocialVec,这是一个学习从社交网络中嵌入社会实体的框架. 这些嵌入式捕捉社会关系,并有利于政治偏见评估和特征预测等任务.

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Assessment of Social Interaction Behaviors
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Assessment of Social Interaction Behaviors

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相关实验视频

Last Updated: Jul 24, 2025

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Author Spotlight: A Novel Setup to Conduct Naturalistic Laboratory Experiments with Real Human Actors in Scenarios
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科学领域:

  • 计算社会科学 计算社会科学
  • 人工智能的人工智能
  • 网络科学 网络科学

背景情况:

  • 事实世界知识在知识库中得到了广泛的代表,但社会世界知识仍然缺乏资源.
  • 有效的沟通和信息处理需要了解社会动态,目前的知识库无法捕捉这些动态.
  • 现有的实体嵌入侧重于事实信息,未能代表社会关系.

研究的目的:

  • 开发一个框架,从社会背景中引出低维实体嵌入.
  • 捕捉基于事实的嵌入错过的世界知识的社会方面.
  • 为理解社会世界知识及其应用创造一个有价值的资源.

主要方法:

  • 开发了SocialVec,这是一个框架,可以从社交网络中学习实体嵌入,并跟踪数据.
  • 在约20万个实体上使用来自130万Twitter用户及其关注账户的数据进行训练的嵌入.
  • 对新闻来源的政治偏见评估和用户的个人特征预测进行了评估.

主要成果:

  • 与基线相比,SocialVec嵌入显示在偏见评估和特征预测任务上具有优势或具有竞争力的性能.
  • 展示了基于事实的实体嵌入不能有效地捕捉社会知识.
  • 学习的社会实体嵌入成功地代表了社会关系和用户特征.

结论:

  • SocialVec提供了一种新的方法,通过实体嵌入来表示社会世界知识.
  • 学习的嵌入对于需要理解社会动态和关系的任务是有价值的.
  • 发布的嵌入式将有助于进一步研究社会世界知识及其应用.