CorpusLD: A Dual-Layer Semantic Extraction Framework and Knowledge Graph Architecture for Scientific Literature with Deterministic Unit Ontology and Authority Disambiguation

Scientific and technical documents published in Portable Document Format (PDF) frequently function as unstructured "data graveyards," creating severe semantic isolation. Conventional Retrieval-Augmented Generation (RAG) and large language model (LLM) chunking pipelines suffer from three fundamental failure modes: top-K context truncation loss, multi-page tabular fragmentation, and numeric collisions caused by superscript citation markers polluting quantitative parameters (e.g., misinterpreting reference indices as numerical quantities). To resolve these challenges, we introduce CorpusLD (Corpus + Linked Data), a production-grade, dual-layer semantic extraction engine and deep knowledge graph architecture. CorpusLD combines a macro-level W3C Schema.org ScholarlyArticle JSON-LD serialization with a micro-level Deep Knowledge Graph spanning 10 formal semantic predicates. The system incorporates a 4-tier layout-aware parser, a section-wise map-reduce orchestrator, and a deterministic scientific unit ontology supporting Base SI, clinical, energy, and compound units. Furthermore, CorpusLD integrates a dynamic REST authority linker communicating with the Research Organization Registry (ROR v2) and Wikidata/MeSH registries. Evaluated across an 8-document multi-disciplinary benchmark corpus (arXiv, IEEE, SINTA, and Springer), CorpusLD achieves 100% structural compliance on the Google Rich Results Test, extracts 100% of complex multi-page tables without boundary corruption, and eliminates superscript citation pollution deterministically. CorpusLD is released as an open-source framework and Python package.