Identois

Identois is a theoretical framework and language for representing and resolving identity across heterogeneous data sources and contexts. It aims to formalize how different records may refer to the same entity and how to prove or disprove that sameness using ontological relations, contextual attributes, and provenance data. The name blends identity and ontology, reflecting its focus on the logical properties of identity within structured knowledge representations.

Core concepts include identity predicates that express sameness and difference, context vectors that describe the attributes relevant to a particular domain, and identity resolution rules that guide when two references should be treated as the same. Identois distinguishes between strong identity, where references have necessary and sufficient equivalence, and weak identity, where equivalence is context-dependent. Provenance and privacy constraints are integrated to ensure auditable decisions.

In practice, Identois is used in data integration, customer data platforms, digital forensics, and research data management to improve deduplication, linkage, and lineage tracking. It is typically implemented as an abstract layer atop existing data models, with mappings to ontologies like OWL or to graph representations, and queryable through SPARQL or equivalent APIs. Several research prototypes demonstrate how Identois can reduce false matches and preserve provenance in complex datasets.

The concept has attracted interest for its potential to unify identity resolution across domains, but critics note challenges in computational complexity, standardization, and privacy risk. As a theoretical framework, Identois serves as a foundation for further work in data governance and semantic interoperability.