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Schemas01:42

Schemas

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A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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A schema is a mental construct that organizes related concepts, allowing the brain to process information efficiently. Upon activation, schemata facilitate assumptions about people or objects.
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Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning, which means that it uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid.
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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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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Graph-based event schema induction in open-domain corpus.

Keyu Yan1,2, Wei Liu1,2, Shaorong Xie1

  • 1School of Computer Engineering and Science, Shanghai University, Shanghai, China.

Peerj. Computer Science
|December 16, 2024
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Summary
This summary is machine-generated.

This study introduces a Graph-Based Event Schema Induction model, improving event clustering and schema generation by incorporating structural graph features. The new method enhances clustering effectiveness and produces highly acceptable event schemas.

Keywords:
Event schema inductionIn-context learningLarge language modelOpen-domain

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Knowledge Representation

Background:

  • Traditional event schema induction methods rely heavily on text features, limiting their clustering capabilities.
  • A formal language for representing events and world knowledge is crucial for artificial intelligence applications.

Purpose of the Study:

  • To propose a novel Graph-Based Event Schema Induction model.
  • To enhance event clustering by extracting structural features from a constructed graph.
  • To generate event schemas using an in-context learning-inspired approach.

Main Methods:

  • Constructed a graph to extract structural features for event schema induction.
  • Developed a method inspired by in-context learning to conceptualize clusters for schema generation.
  • Evaluated clustering performance using Adjusted Rand Index (ARI), normalized mutual information (NMI), accuracy (ACC), and BCubed-F1 metrics.

Main Results:

  • The Graph-Based Event Schema Induction model demonstrated improved clustering effectiveness compared to existing methods.
  • Generated event schemas achieved a highly acceptable ratio, indicating practical utility.
  • The model successfully integrated structural graph features into the event schema induction process.

Conclusions:

  • The proposed Graph-Based Event Schema Induction model offers a significant advancement in the field.
  • Incorporating structural information from graphs enhances the capability of event schema induction.
  • The method provides a promising approach for knowledge representation and event modeling.