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Knowledge-graph-based explainable AI: A systematic review.

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Knowledge graphs (KGs) enhance explainable AI (XAI) by extracting features and relationships, particularly in healthcare. This review categorizes KG applications within XAI systems.

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Area of Science:

  • Artificial Intelligence
  • Data Science
  • Information Science

Background:

  • Knowledge graphs (KGs) offer semantic representations of data through hierarchical structures.
  • KGs facilitate the integration of diverse information sources.
  • Interpretable and explainable Artificial Intelligence (AI) systems benefit from structured knowledge representation.

Purpose of the Study:

  • To systematically review recent literature on the application of KGs in eXplainable AI (XAI).
  • To categorize the utilization of KGs within different stages of XAI models (pre-model, in-model, post-model).
  • To identify prevalent domains and methodologies employing KGs for AI explainability.

Main Methods:

  • A systematic review of recent publications was conducted.
  • A framework was designed to categorize KG usage into feature extraction, relationship extraction, KG construction, and KG reasoning.
  • The application stages within XAI (pre-model, in-model, post-model) were identified for each category.

Main Results:

  • KGs are predominantly used in pre-model XAI for feature and relation extraction.
  • KG reasoning and inference are significant in post-model XAI applications.
  • The healthcare domain shows notable utilization of KGs for explaining XAI models.

Conclusions:

  • Knowledge graphs are valuable tools for enhancing the explainability of AI systems.
  • Specific applications of KGs, such as feature extraction and reasoning, are key to improving AI interpretability.
  • Future research can further explore KG integration across all stages of XAI, especially in specialized domains like healthcare.