# Knowledge Graph Construction

AI systems for scientific discovery usually learn only from published positive results, leaving them structurally blind to failures and suppressed directions. A three-layer operational protocol turns that gap into executable steps: it scores theoretically possible but undocumented relationships in a knowledge graph, uses an adversarial classifier to surface features systematically suppressed in unpublished, rejected or failed data, and generates hypotheses that diverge from consensus yet pass viability filters. In a CPEB3/CPEB4 neurobiology case, it flagged four structural blind spots and produced a testable hypothesis about astrocytic translation during sleep and active forgetting.

AI systems for scientific discovery usually learn only from published positive results, leaving them structurally blind to failures and suppressed directions. A three-layer operational protocol turns that gap into executable steps: it scores theoretically possible but undocumented relationships in a knowledge graph, uses an adversarial classifier to surface features systematically suppressed in unpublished, rejected or failed data, and generates hypotheses that diverge from consensus yet pass viability filters. In a CPEB3/CPEB4 neurobiology case, it flagged four structural blind spots and produced a testable hypothesis about astrocytic translation during sleep and active forgetting.

The work presents a pipeline that uses a code-specialised LLM (based on Qwen3-Coder-30B-A3B-Instruct) to annotate source files under an open taxonomy, extracting algorithms, paradigms, design patterns, and application domains, then grounds these in Wikidata through a three-stage procedure (deterministic SPARQL, a Deep Research Agent for the long tail, and parent-of hierarchy rollup), with a calibrated quality-assurance protocol combining a human gold set and an LLM-as-a-judge filter; applied to the Stack-Edu corpus it yields CodeGraph with roughly 158 million nodes (about 145 million file nodes, about 63,000 extracted concept entities, and roughly 19,800 grounded Wikidata entities) and about 1 billion typed edges across 14 programming languages.

The work presents a pipeline that uses a code-specialised LLM (based on Qwen3-Coder-30B-A3B-Instruct) to annotate source files under an open taxonomy, extracting algorithms, paradigms, design patterns, and application domains, then grounds these in Wikidata through a three-stage procedure (deterministic SPARQL, a Deep Research Agent for the long tail, and parent-of hierarchy rollup), with a calibrated quality-assurance protocol combining a human gold set and an LLM-as-a-judge filter; applied to the Stack-Edu corpus it yields CodeGraph with roughly 158 million nodes (about 145 million file nodes, about 63,000 extracted concept entities, and roughly 19,800 grounded Wikidata entities) and about 1 billion typed edges across 14 programming languages.