# Source Code Mining

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.