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Related Concept Videos

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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Schemata01:17

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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.
Two types of schemata are:
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Self-Schemas02:16

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In general, 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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Structural Classification of Joints01:20

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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.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Heuristics01:21

Heuristics

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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
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Semi-automatic construction of heterogeneous data schema based on structure and context-aware recommendation.

Nan Yin1, Junheng Liang1, Xi Guo2,3

  • 1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.

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This study introduces a semi-automatic method for creating scientific data schemas, improving efficiency by 50.5% and reducing schema proliferation by 16.5%. The approach uses context-aware recommendations for heterogeneous material data.

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Area of Science:

  • Data Science
  • Materials Informatics
  • Scientific Data Management

Background:

  • Scientific data publishing requires flexible schema customization for diverse data types.
  • Current platforms lead to schema proliferation and complex creation processes.
  • Heterogeneous material data presents unique schema construction challenges.

Purpose of the Study:

  • To develop a semi-automatic method for constructing heterogeneous material data schemas.
  • To enhance the efficiency and reduce the complexity of data schema creation.
  • To address schema proliferation in data publishing platforms.

Main Methods:

  • Proposed a schema fragment tree structure for hierarchical data representation.
  • Implemented context-aware recommendation through subtree matching.
  • Utilized fragment index, semantic search, and tree editing distance for similarity calculation.

Main Results:

  • The proposed algorithm outperformed TF-IDF and BM25 baselines in schema matching (precision, recall, F1-score).
  • Achieved a 50.5% improvement in schema creation efficiency.
  • Reduced schema proliferation by 16.5% compared to manual methods.

Conclusions:

  • The semi-automatic method effectively constructs heterogeneous material data schemas.
  • Context-aware recommendations significantly improve schema creation efficiency and reduce proliferation.
  • The approach offers a scalable solution for managing diverse scientific data structures.