KG4XAI — Knowledge Graphs for Explainable Artificial Intelligence: Taxonomies, Methodologies, and Future Research Directions | by Adnan Masood, PhD. | Medium

You're reading for free via Adnan Masood, PhD.'s Friend Link. Become a member to access the best of Medium. Member-only story 20 1 Listen Share Complimentary Reading TL;DR: Knowledge Graphs for Explainable AI (KG4XAI) represents a paradigm shift from black-box machine learning to transparent, human-understandable AI systems. By integrating structured knowledge with neural networks, KG4XAI enables AI to explain its decisions using domain knowledge, real-world relationships, and semantic context rather than raw statistical patterns. Key Findings: KG4XAI operates across three integration stages: pre-model (feature engineering), in-model (architectural embedding), and post-model (explanation generation) Knowledge graphs provide semantic grounding that transforms low-level feature attributions into human-meaningful explanations Modern approaches combine large language models with knowledge graphs for conversational, context-aware explanations Critical challenges include knowledge quality, m

You're reading for free via Adnan Masood, PhD.'s Friend Link. Become a member to access the best of Medium. Member-only story 20 1 Listen Share Complimentary Reading TL;DR: Knowledge Graphs for Explainable AI (KG4XAI) represents a paradigm shift from black-box machine learning to transparent, human-understandable AI systems. By integrating structured knowledge with neural networks, KG4XAI enables AI to explain its decisions using domain knowledge, real-world relationships, and semantic context rather than raw statistical patterns. Key Findings: KG4XAI operates across three integration stages: