#knowledge-graphs
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DysLexLens: A Framework for Analyzing Dyslexic Learner AI Experiences
DysLexLens is an end-to-end, evidence-traceable framework that uses dictionary-driven filtering and knowledge graphs to analyze how dyslexic learners interact with AI tools via online forums.
Building Context Graphs for AI Agent Decision-Making
Context graphs improve agent accuracy by storing 'decision traces'—the reasoning and historical precedents behind past outcomes—allowing agents to perform structural similarity searches alongside standard semantic retrieval.
AI EngineerBuilding Decision-Aware AI Agents with Context Graphs
Context graphs move AI agents beyond simple knowledge retrieval by embedding policies, rules, and historical precedents, enabling agents to perform explicit risk-value analysis before acting.
AI EngineerFormalizing Agentic Knowledge Graphs for LLM Discoverability
The paper proposes a formal framework for 'Agentic KG Affordances,' enabling AI agents to programmatically discover and interact with knowledge graphs by standardizing how knowledge is exposed and queried.
Beyond RAG: Building Hybrid Knowledge Architectures
RAG is effective for static, unstructured retrieval but fails at reasoning, structured data, and long-term memory. Production systems require hybrid architectures that combine retrieval with knowledge graphs and persistent state.
NLP Progression: Word Clouds to Knowledge Graphs
Build semantic systems from text by progressing: word cloud (frequency) → TF-IDF (importance) → co-occurrence graph (relationships) → knowledge graph (durable meaning). Skip intermediates and your graph stores noise.
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