American Journal of Advanced Multidisciplinary Innovation and Research

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Semantic Knowledge Graphs for Integrating Fragmented Institutional Evidence

Author(s) Dr. Ethan Marshall
Country United States
Abstract Institutional evidence is frequently dispersed across policy documents, research reports, operational databases, spreadsheets, administrative records, project repositories, technical systems, archival collections, and departmental knowledge bases. Although these sources may individually contain valuable information, differences in terminology, identifiers, schemas, formats, provenance, access conditions, and update cycles can prevent institutions from constructing a coherent evidential view. Semantic knowledge graphs provide a promising approach by representing entities, concepts, claims, sources, and their relationships within a machine-interpretable graph while preserving links to originating evidence. This study develops a Semantic Institutional Evidence Integration Framework (SIEIF) combining ontology-mediated integration, entity resolution, semantic mapping, provenance modeling, graph validation, contradiction preservation, access governance, and evidence-aware retrieval. The framework draws upon contemporary Semantic Web infrastructure, including RDF, OWL, SPARQL, SHACL, and PROV-O. W3C's 2026 RDF 1.2 and SPARQL 1.2 technical specifications further reinforce the continuing development of graph-based semantic data exchange and cross-source querying. Because no verified institutional deployment dataset was available, the study adopts an explicitly simulation-based methodology.
Four hypothetical integration architectures are compared across six dimensions: semantic alignment, entity resolution, provenance traceability, cross-source retrieval, contradiction handling, and governance observability. The simulated Semantic Evidence Integration Readiness Index increases from 33 for a fragmented evidence environment to 89 for a provenance-aware semantic knowledge graph. Recent cross-institutional biomedical implementations demonstrate that RDF-based knowledge-graph pipelines can support semantic interoperability across heterogeneous source systems while maintaining local governance and validation. The study concludes that institutional knowledge graphs should not merely collapse heterogeneous evidence into unified facts. Stronger systems preserve provenance, uncertainty, disagreement, version history, and access rights so that integrated evidence remains interpretable, auditable, and contestable.
Keywords semantic knowledge graphs, institutional evidence, semantic interoperability, ontology, provenance, entity resolution, evidence integration, RDF, knowledge management
Field Engineering
Published In Volume 3, Issue 2, March-April 2022
Published On 2022-04-11
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