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

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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

Member-only story 84 min read May 30, 2025 -- Building Trust in AI Through Transparent Decision-Making — an In-Depth Analysis of Symbolic-Neural Integration Approaches for Transparent Machine Learning Systems Complimentary Reading Press enter or click to view image in full size 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,…

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