Constructing a High-precision and Semantically Rich BGP Community Dictionary via Large Language Models (LLMs)

BGP communities are essential for inter-domain routing control, but their semantics are often buried in noisy, unstructured operator documentation. Manual inspection and annotation is an laborious process that does not scale. Meanwhile, existing automated methods, mainly relying on regular expressions, cannot reliably interpret semantic meaning or handle placeholder-style formats. We develop an LLM-based framework for extracting high-precision BGP community semantics from heterogeneous operator documentation. Our framework combines format-aware preprocessing to filter community-relevant content from noisy operator documents, schema-guided extraction to generate structured output, and consensus-based aggregation across models to mitigate hallucinations and false extractions. Using a manually-annotated benchmark dataset containing 9,244 community value–meaning pairs, our framework achieves 85-99% precision across different extraction tasks and substantially outperforms prior state-of-the-art methods on placeholder communities and preserving semantic accuracy. Using our framework, we construct one of the largest open-access available BGP community dictionaries, containing more than 29.9K semantically annotated communities across 766 ASes. Finally, we perform the first large-scale analysis of semantic variability in operator-defined BGP communities using our dictionary. We release our code, benchmark, and constructed dictionary to support future research.