Research and reference foundation
The Manifesto, subsequent works, MVDIO, the Reconstruction Failure Modes taxonomy, Reference Vocabulary v0.1.0 and Reference Model v0.3.1.
Evidence · publications · reference layer
From the manifesto to a diagnostic and machine-readable architecture.
DIO/ODI has progressed from public articulation to a minimal analytical model, a multi-level Reconstruction Failure Modes taxonomy and a versioned Reference Model.
Three states
Each claim belongs to a different evidence layer.
DIO/ODI distinguishes public artifacts, a methodologically prepared intervention framework and empirical outcomes that require external measurement.
DIO intervention and a pilot design
Baseline measurement, representation mapping, diagnosis, prioritised interventions, pre/post evidence and retrospective analysis form a prepared procedure for external pilot testing.
Empirical performance, transfer and market application
External evaluation concerns causal contribution, transfer across segments, metric reliability, willingness to pay, scalability and the appropriate product form.
DIO/ODI development path
Each work refines a different layer of the same problem.
The publication path moves from the public formulation of the concept to a minimal model, a failure taxonomy and reference infrastructure. Links point to the live manifesto or a persistent record of the published work.
Manifesto · 27 July 2026
Manifest of Digital Identity Optimization (DIO) and Ontology of Digital Identity (ODI): Canonical Multilingual Edition
Definition of DIO, ODI, their shared terminology and the K6 model in a canonical multilingual edition.
Conceptual Framework · 7 August 2026
Digital Identity Optimization (DIO): A Conceptual Framework for Entity-Based Visibility in the Age of AI-Mediated Search
Development of DIO as a shift from optimizing individual surfaces toward entity-based visibility.
Entity Resolution Crisis · 2 September 2026
The Crisis of Entity Resolution in Heterogeneous AI Environments: Data, Financial, and Semantic Dimensions
Analysis of entity-resolution anomalies, identity collapse and semantic vulnerability.
MVDIO · 8 October 2026
Minimum Viable Digital Identity Optimization (DIO)
An adversarial reduction of DIO to a referent, a distributed representational state and observer reconstruction; K6 remains a finer-grained candidate model.
Taxonomic Note · 9 October 2026
From Entity Resolution Anomalies to Reconstruction Failure Modes: A Taxonomic Note Following Minimum Viable Digital Identity Optimization (DIO)
A multi-level taxonomy of referential, semantic, temporal and contextual reconstruction failures.
Reference Model · 11 October 2026
DIO/ODI Reference Model v0.3.1
A versioned release of Reference Vocabulary v0.1.0, both Application Mappings and related machine-readable artifacts.
Reference and application layer
Concepts have a canonical address. Their applications use explicit, reviewable relations.
Reference Vocabulary defines the meaning of DIO/ODI concepts. Separate Application Mappings project them into a RAG pipeline and the Heterogeneous Entity Matching taxonomy without changing their canonical definitions. Each mapping is available as a frontend, Markdown, JSON-LD and Turtle RDF.
Reference Vocabulary v0.1.0
Stable URIs, definitions, relationships and provenance within the DIO/ODI namespace.
RAG × DIO and HEM × DIO
Two separate analytical projections connect Reconstruction Failure Modes to a RAG pipeline and the heterogeneity of input representations to possible reconstruction consequences.
Markdown, JSON-LD and Turtle RDF
The machine-readable layer distinguishes the source condition, target concept and relation type. The formulations “may contribute to” and “may increase” express possibility or susceptibility, not equivalence or deterministic causality.
Distribution architecture
One corpus, different modes of access.
The distribution infrastructure supports discoverability, provenance control, citability and access for subsequent testing. Effectiveness measurement takes place in a separate empirical layer.
Live nodal point
The current Czech manifesto, changelog and evolving conceptual layer.
Canonical citation record
A versioned, persistent edition deposited on Zenodo.
Machine-accessible corpus
Distribution for machine learning, file inspection and programmatic access.
Comparison of language projections
An interactive data layer for exploration and basic structural inspection.
Secondary scholarly distribution
Access to the manifesto and related work for specialist audiences.
Application examples
Public materials explore uses of DIO for open source, enterprise and NGOs.
Evidentiary scope
An asset documents articulation. Attention expands reach. Measurement captures performance.
- Distribution platforms make the same corpus accessible to different audiences and systems.
- A DOI creates a persistent and citable record.
- The publication corpus documents the framework’s public articulation and development.
- Crawling confirms machine access to the content.
- The author’s own brand serves as a relevant demonstrator and development case.
- General statistics on data quality define the context of the problem DIO/ODI addresses.
Separate pilot measurement addresses the effectiveness, repeatability and transferability of a specific intervention.
Validation agenda
The next evidence must be bounded, transparent and decision-oriented.
A pilot should define its confirmation and falsification conditions, tracked metrics and interpretive boundaries in advance. Its outcome should remain decision-relevant even when the expected change does not occur.
Baseline
An observable map of the entity, representations, relationships, sources and error patterns.
Interpretability check
A second evaluator or technical counterpart applies the same rules to a selected sample.
Targeted intervention
A small number of changes clearly linked to the diagnosed problem.
Pre/post evidence
A record of changes in predefined signals, completed steps and implementation barriers.
Decision
Continue, narrow the segment, change the metric, revise the methodology or remove a specific unsupported element of the model.
Subsequent empirical work
The reference architecture gives a pilot a more precise shared language.
MVDIO, the failure taxonomy and the Reference Model make it possible to define the referent, observation domain, expected reconstruction and specific failure modes before an intervention. A pilot can therefore track more precisely named changes.
DIO/ODI evidence
The primary sources and reference layer are public. The next application can be developed together.
The partner brings a case, environment or expert critique. DIO/ODI brings a published analytical architecture, reference model and intervention design.