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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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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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A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
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Autonomous schema markups based on intelligent computing for search engine optimization.

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Summary
This summary is machine-generated.

This study introduces an autonomous method for identifying entities in short web page texts, improving semantic search. The approach uses deep learning and machine learning, achieving high accuracy and reducing manual effort.

Keywords:
Content discoverySchema.orgSearch engine optimizationSemantic searchUnstructured data

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Area of Science:

  • Computer Science
  • Information Retrieval
  • Artificial Intelligence

Background:

  • Search engines are integrating semantics for complex queries, requiring entity identification from web content.
  • Unstructured data and text fragments in HTML tags pose challenges for traditional entity recognition.
  • Ontologies can structure web data but require significant resources and expertise for implementation.

Purpose of the Study:

  • To propose an autonomous approach for identifying entities from short text fragments on web pages.
  • To populate semantic models using identified entities based on a specific ontology.
  • To reduce the manual workload in practical applications of semantic data enrichment.

Main Methods:

  • Utilized a long short-term memory (LSTM) deep learning network.
  • Employed the random forest machine learning algorithm for entity prediction.
  • Applied the approach to a public dataset of academic web pages.

Main Results:

  • Achieved an overall accuracy of 0.94 on the test dataset.
  • Demonstrated potential for automated prediction with limited training samples.
  • Indicated significant reduction in manual workload for practical applications.

Conclusions:

  • The proposed autonomous entity identification method is effective for short web page texts.
  • The approach successfully populates semantic models, enhancing web data structure.
  • High accuracy suggests a viable solution for large-scale semantic data enrichment.