Gnoselia- An Institutional Knowledge Intelligence Layer Based on AI Hybrid Retrieval

Institutional knowledge is increasingly distributed across heterogeneous and weakly connected sources, including document repositories, relational databases, scientific literature, administrative archives, and operational information systems. Although these resources may be technically accessible, the relationships between data, analyses, documents, institutions, concepts, and supporting evidence often remain implicit, limiting their effective reuse for research and decision-making. This paper presents Gnoselia, a proposed institutional knowledge intelligence architecture designed to address this fragmentation through hybrid retrieval and typed knowledge graphs. The architecture integrates nine functional layers spanning heterogeneous data ingestion, document processing, semantic representation, vector indexing, knowledge graph construction, structured data access, hybrid retrieval, artificial intelligence orchestration, and evidence validation. Retrieval combines dense vector search, graph traversal, structured querying, lexical search, and, when required, federated access to external scientific services through a Model Context Protocol gateway. A central design principle is that generated claims should remain traceable to identifiable evidence. The architecture therefore incorporates provenance management, claim-to-source linkage, access-aware retrieval, evidence ranking, and abstention when available evidence is insufficient. An optional bounded agentic layer is proposed to re-initiate evidence acquisition when retrieved information is incomplete, outdated, or contradictory. A subset of the architecture is currently implemented as a Neo4j-based interaction graph that preserves relationships among user questions, generated answers, consulted documents, sessions, and user evaluations. The broader hybrid retrieval, federation, and evidence-validation architecture remains under development. We hypothesize that combining semantic retrieval with explicit relational knowledge representation can improve retrieval quality, answer faithfulness, traceability, and multi-hop reasoning compared with vector-only Retrieval-Augmented Generation. Seven falsifiable operational hypotheses and a pre-specified five-arm evaluation framework are proposed to test these claims. No comparative empirical performance results are reported at this stage.