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A review of feature selection strategies utilizing graph data structures and Knowledge Graphs.

Sisi Shao1, Pedro Henrique Ribeiro2, Christina M Ramirez1

  • 1Department of Biostatistics, Fielding School of Public Health at University of California, Los Angeles, 650 Charles E Young Dr S, Los Angeles, CA 90095-1772, United States.

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Summary

Feature selection in Knowledge Graphs (KGs) enhances machine learning models for better insights. This review highlights scalability, accuracy, and interpretability as key for advancing KG feature selection (FS) and its applications.

Keywords:
Knowledge Graphsdeep learningexplainable AIfeature selectionprecision medicine

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Area of Science:

  • Computer Science
  • Bioinformatics
  • Artificial Intelligence

Background:

  • Feature selection (FS) is crucial for Knowledge Graphs (KGs) in fields like biomedical research and NLP.
  • KGs enhance machine learning (ML) model efficacy, hypothesis generation, and interpretability.

Purpose of the Study:

  • To review methodologies for feature selection in Knowledge Graphs.
  • To emphasize the role of FS in improving ML model performance and interpretability.
  • To catalyze innovation in KG feature selection for diverse applications.

Main Methods:

  • Comprehensive review of existing feature selection techniques for KGs.
  • Analysis of critical factors including scalability, accuracy, and interpretability.
  • Exploration of domain knowledge integration and multi-objective optimization.

Main Results:

  • Identified scalability, accuracy, and interpretability as critical for KG FS.
  • Highlighted the potential of integrating domain knowledge to refine FS.
  • Showcased the impact of multi-objective optimization and interdisciplinary collaboration.

Conclusions:

  • Feature selection is vital for advancing KG-based analytical models.
  • Future directions include scalable, dynamic FS algorithms and explainable AI integration.
  • KG feature selection holds transformative potential for precision medicine and other fields.