Operational Substrate Architecture for Bidirectional Longitudinal Knowledge Anchoring: A Multi-Modal Personal-Scale Implementation Layer
Year: 2026
Type: article
Abstract: This deposit anchors the operational substrate layer for a longitudinal methodological program documenting an architectural specification for multi-modal personal-scale knowledge-graph construction with subject-authored semantic anchoring. Methodological architecture specification; NOT a clinical, therapeutic, or diagnostic claim, and not medical advice. The substrate addresses four properties in combination: (i) ingestion of multi-modal personal data streams including visual, audio, and aggregated sensor signals, with architectural provisions for additional non-invasive signal modalities; (ii) semantic anchoring through a bidirectional methodology in which the subject authors the term ontology and validates each anchor through forward and backward probing; (iii) local hardware sovereignty in which raw modality data and embedding operations remain on infrastructure under the subject's control, with encryption at rest and optional air-gap provisions for the most sensitive streams; (iv) long-duration archival design with data-portability provisions across heterogeneous storage systems. No raw special-category personal data within the meaning of GDPR Article 9 is released; raw streams remain restricted. The substrate operates downstream from a separately-anchored methodology layer (DOI 10.5281/zenodo.20309991, restricted). Differentiation against adjacent prior and contemporary work: each constituent property has predecessor work; in a May 2026 survey by the author, no publicly available work combining all four properties in this specific configuration was identified. Access to the deposit files is restricted; the metadata is publicly accessible to establish a defensive priority anchor. AI-transparency (EU AI Act, Art. 50): prepared with substantial large-language-model assistance under full human editorial review; the human author retains sole responsibility. Author: Andreas Ehstand (Independent Researcher), ORCID 0009-0006-3773-7796.
Author: Ehstand, Andreas
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7162525852
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