The Crisis of Entity Resolution in Heterogeneous AI Environments: Data, Financial, and Semantic Dimensions
Auteurs : Daniel Beránek
Résumé scientifique
AbstractThis preprint examines the crisis of entity resolution in heterogeneous AI environments across data, financial, and semantic dimensions. It develops an entity turn in AI: a shift from isolated tokens, documents, and embeddings toward the question of whether systems can reliably identify and reconstruct the same entity across sources, time, languages, and contexts.The paper synthesizes research on entity linking, entity matching, temporally evolving knowledge bases, retrieval-augmented generation (RAG), and multi-agent LLM systems with public enterprise evidence on data quality and AI readiness. It identifies four interconnected anomalies: identity collapse under name ambiguity and popularity bias; semantic fragmentation across data silos; temporal discontinuity when entities change while indexes remain static; and semantic vulnerability when retrieved context is incomplete, conflicting, or manipulated. In RAG and agentic systems, these failures may propagate from indexing and retrieval through generation to subsequent tool calls and apparent verification.The final section examines Digital Identity Optimization (DIO) and the Ontology of Digital Identity (ODI) as an author-formulated theoretical framework for governing the integrity of digital identity. DIO/ODI are not presented as empirically validated technology or as a ready-made solution to entity resolution, temporal linking, or RAG security. They are proposed as a testable integrity hypothesis for the coherent reconstruction of digital identity across people, systems, languages, time, and contexts.Record and version Version 1.0.3 (Figshare); corresponds to Version 1.0.0 of the canonical Zenodo record.This Figshare item distributes the same preprint as the canonical Zenodo record. For archival reference and citation, use: Beránek, D. (2026). *The Crisis of Entity Resolution in Heterogeneous AI Environments: Data, Financial, and Semantic Dimensions*. Zenodo. https://doi.org/10.5281/zenodo.22257504A companion version of this manuscript is also available on SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7396398AI disclosureThis manuscript was developed through a human-led process of research design, source selection, comparative AI-assisted exploration, and substantive authorial revision. Manus AI was used as a synthesis and drafting instrument on the basis of the author's curated inputs and continuous revisions. Other AI-assisted outputs served only as provisional analytical material and were incorporated only after authorial selection, verification against cited sources where applicable, and further revision. Perplexity AI additionally supported the technical and administrative preparation of this record and its distribution, including citation formatting, metadata structuring, and platform-specific publication procedures across SSRN and Zenodo. The DIO/ODI framework and all critical intellectual decisions remain the sole work of the author, who takes full responsibility for the manuscript's content, interpretation, source use, conceptual framing, and conclusions.
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Résumé scientifique
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Mis à jour le 1 novembre 2026.