Open Biomedical Knowledge Graphs at Scale: Construction, Federation, and AI Agent Access with Samyama Graph Database

Abstract

Biomedical knowledge is fragmented across siloed databases---Reactome, STRING, Gene Ontology, ClinicalTrials.gov, DrugBank, DGIdb, SIDER, PubMed, ClinVar, ChEMBL, OpenTargets, and more. We present an open, reproducible biomedical knowledge graph ecosystem built on Samyama, a Rust-native graph database: three foundational graphs detailed end-to-end---Pathways KG (118,686 nodes, 834,785 edges), Clinical Trials KG (7,774,446 nodes, 26,973,997 edges), and Drug Interactions KG (32,726 nodes, 191,970 edges)---and their expansion into a 22-graph, 2-cohort merged biomedical store (328,130,739 nodes and 1,441,425,733 imported edges, including 295,000 synthetic OMOP CDM patients from Synthea). First, we describe a reproducible ETL and portable .sgsnap snapshot pattern where graphs load alone or compose into one store. Second, we analyze single-store cross-KG integration via query-time property joins and load-time identifier deduplication (--dedup-keys): across all 24 snapshots (Samyama-Graph v1.13.0), 328.1M nodes and 1.44B edges load in 2,269.1 s (37m 49s) within 366.5 GB peak RSS (254 B/edge), merging 132,511 cross-source entities across five audited keys (mondo_id,uniprot_id,go_id,drugbank_id,symbol)---87.8% of the 150,965 Bound A sound-identifier ceiling, with zero conflicting-ID refusals. On a frozen 81-query suite, 62 queries return results (17 data-gap empties, 2 timeouts); on an expanded 200-query oracle suite, 195 of 200 pass (97.5%). Third, we introduce schema-driven Model Context Protocol (MCP) server generation: on the 40-question BiomedQA benchmark over the 3-KG subset, domain-specific MCP tools achieve 98% accuracy vs. 85% for schema-aware text-to-Cypher and 75% for standalone GPT-4o, with zero schema errors.