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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Related Experiment Videos

Automatic Structuring of Ontology Terms Based on Lexical Granularity and Machine Learning: Algorithm Development and

Lingyun Luo1,2, Jingtao Feng1, Huijun Yu3

  • 1School of Computer Science, University of South China, Hengyang, China.

JMIR Medical Informatics
|October 31, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for generating biomedical ontologies, using machine learning to predict relationships between anatomical concepts and create hierarchical structures efficiently.

Keywords:
Foundational Model of Anatomyautomatic structuringlexical granularitymachine learningontology

Related Experiment Videos

Area of Science:

  • Biomedical informatics
  • Computational biology
  • Ontology engineering

Background:

  • Manual creation and maintenance of biomedical ontologies are time-consuming and resource-intensive.
  • Automated tools are crucial for efficient ontology development and lifecycle management.

Purpose of the Study:

  • To develop an innovative method for automatically generating taxonomy and partonomy structures from concept names.
  • To address the labor-intensive nature of manual biomedical ontology development.

Main Methods:

  • Utilized word embedding methods to predict direct relations between concept names.
  • Trained Convolutional Neural Networks (CNN) and Bidirectional Long Short-term Memory Networks (Bi-LSTM) machine learning models.
  • Introduced a granularity-based method to identify semantic structures among concept groups using trained models.

Main Results:

  • CNN and Bi-LSTM models achieved high performance (F1 > 0.91) in predicting pairwise concept relations.
  • The proposed granularity-based approach with Bi-LSTM achieved an average F1 of 0.79 in structuring concept groups.
  • Outperformed traditional pairwise-based methods in generating semantic structures.

Conclusions:

  • Successfully developed an automated method for predicting hierarchical relationships between concept names.
  • Invented a novel methodology for automatically structuring groups of concept names.
  • This research provides a foundation for future advancements in automatic biomedical ontology creation and enrichment.