DIO/ODI Reference Model

Ranked by how close their title and abstract are to this paper’s. Select some to read them together.

Closest papers

1. Close Same question Not relevant

   Manifest of Digital Identity Optimization (DIO) and Ontology of Digital Identity (ODI): Canonical Multilingual Edition

   Daniel Beránek · 2026 · Zenodo (CERN European Organization for Nuclear Research) · AI summary available

   The canonical edition defines DIO and ODI, the same digital-identity framework for which this paper supplies a machine-readable reference vocabulary.

   In short

   The abstract presents DIO and ODI as a multilingual framework for conceptualizing and maintaining digital identity, but reports no empirical evaluation.

2. Related Same question Not relevant

   Digital Identity Optimization (DIO): A Conceptual Framework for Entity-Based Visibility in the Age of AI-Mediated Search

   Daniel Beránek · 2026 · Zenodo (CERN European Organization for Nuclear Research) preprint · AI summary available

   This paper theorizes DIO as an entity-based visibility framework, while the reference model formalizes vocabulary for DIO and ODI.

   In short

   The paper proposes Digital Identity Optimization (DIO), a conceptual framework for making entities consistently identifiable and recommendable across human and AI-mediated systems.

3. Related Broader context Not relevant

   From Entity Resolution Anomalies to Reconstruction Failure Modes: A Taxonomic Note Following Minimum Viable Digital Identity Optimization (DIO)

   Daniel Beránek · 2026 · Zenodo (CERN European Organization for Nuclear Research) preprint · AI summary available

   This taxonomy names digital-identity reconstruction failure modes, a neighbouring topic to the reference model’s DIO and ODI vocabulary.

   In short

   The note organizes reconstruction failure modes into four diagnostic domains and distinguishes realized failures from susceptibility to destabilization.

4. Related Broader context Not relevant

   Digital Twin sensors in cultural heritage applications

   Franco Niccolucci, Achille Felicetti · 2024 · Sensors 2024, 24, 3978 · cited 47 times · Not summarized yet

   This paper extends an ontology for reactive cultural-heritage digital twins, applying ontology modelling to a different digital-identity-adjacent domain.

   Abstract

   The paper concerns the extension of the Heritage Digital Twin Ontology introduced in previous work to describe the reactivity of digital twins used for cultural heritage documentation by including the semantic description of sensors and activators and all the process of interacting with the real world. After analysing previous work on the use of digital twins in cultural heritage, a summary description of the Heritage Digital Twin Ontology is provided, and the existing applications of digital twins to cultural heritage are overviewed, with references to reviews summarizing the large production of scientific contributions on the topic. Then a novel ontology, named Reactive Digital Twin Ontolo…

5. Related Broader context Not relevant

   IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being

   Amelie Gyrard, Seyedali Mohammadi, Manas Gaur et al. · 2024 · Artificial Intelligence · preprint, not peer reviewed · cited 5 times · AI summary available

   This paper uses knowledge graphs and standards for preventive mental-health monitoring, rather than defining DIO and ODI concepts.

   Abstract

   Sustainable Development Goals (SDGs) give the UN a road map for development with Agenda 2030 as a target. SDG3 "Good Health and Well-Being" ensures healthy lives and promotes well-being for all ages. Digital technologies can support SDG3. Burnout and even depression could be reduced by encouraging better preventive health. Due to the lack of patient knowledge and focus to take care of their health, it is necessary to help patients before it is too late. New trends such as positive psychology and mindfulness are highly encouraged in the USA. Digital Twins (DTs) can help with the continuous monitoring of emotion using physiological signals (e.g., collected via wearables). DTs facilitate monito…

6. Related Broader context Not relevant

   Providing Assurance and Scrutability on Shared Data and Machine Learning Models with Verifiable Credentials

   Iain Barclay, Alun Preece, Ian Taylor et al. · 2021 · Machine Learning · cited 24 times · AI summary available

   This system uses verifiable credentials to document data and model provenance, while the reference model records provenance for vocabulary concepts.

   Abstract

   Adopting shared data resources requires scientists to place trust in the originators of the data. When shared data is later used in the development of artificial intelligence (AI) systems or machine learning (ML) models, the trust lineage extends to the users of the system, typically practitioners in fields such as healthcare and finance. Practitioners rely on AI developers to have used relevant, trustworthy data, but may have limited insight and recourse. This paper introduces a software architecture and implementation of a system based on design patterns from the field of self-sovereign identity. Scientists can issue signed credentials attesting to qualities of their data resources. Data c…

7. Related Broader context Not relevant

   Referring to Any Person

   Qing Jiang, Lin Wu, Zhaoyang Zeng et al. · 2025 · Computer Vision and Pattern Recognition · cited 27 times · Not summarized yet

   This paper studies identifying people from natural-language descriptions, whereas the reference model defines concepts for digital identity optimization.

   Abstract

   Humans are undoubtedly the most important participants in computer vision, and the ability to detect any individual given a natural language description, a task we define as referring to any person, holds substantial practical value. However, we find that existing models generally fail to achieve real-world usability, and current benchmarks are limited by their focus on one-to-one referring, that hinder progress in this area. In this work, we revisit this task from three critical perspectives: task definition, dataset design, and model architecture. We first identify five aspects of referable entities and three distinctive characteristics of this task. Next, we introduce HumanRef, a novel da…

8. Related Broader context Not relevant

   Understanding Our People at Scale

   Tam N. Nguyen · 2020 · Computers and Society · preprint, not peer reviewed · Not summarized yet

   This paper combines machine learning with ontology-based reasoning to analyze employee psychology, applying ontologies to a different problem.

   Abstract

   Human psychology plays an important role in organizational performance. However, understanding our employees is a difficult task due to issues such as psychological complexities, unpredictable dynamics, and the lack of data. Leveraging evidence-based psychology knowledge, this paper proposes a hybrid machine learning plus ontology-based reasoning system for detecting human psychological artifacts at scale. This unique architecture provides a balance between system's processing speed and explain-ability. System outputs can be further consumed by graph science and/or model management system for optimizing business processes, understanding team dynamics, predicting insider threats, managing tal…