Dataonto

Dataonto is a conceptual framework and ontology-development approach designed to model and align data from diverse sources through formal semantic representations. It aims to provide a lightweight, reusable ontology layer that can be overlaid on relational, NoSQL, and semi-structured data to enable consistent interpretation, discovery, and interoperability across systems.

In scholarly and practical discussions, dataonto is often presented as a design pattern rather than a standardized product. It emphasizes creating a core ontology of generic concepts and extending it with domain-specific modules to cover particular business contexts, such as finance, healthcare, or manufacturing.

Architecturally, dataonto typically relies on standard semantic web formalisms such as RDF and OWL. A core ontology defines universal concepts like Entity, Attribute, and Relationship, while domain extensions capture specialized terms and constraints. Mappings from source schemas to ontology concepts are expressed as alignment rules or OBDA-style mappings, enabling semantic access to underlying data. Provenance, versioning, and data quality annotations are commonly included to support governance and provenance tracking.

Common use cases include semantic data integration, enhanced data discovery and search, metadata management, and data lineage reporting. The approach is compatible with SPARQL-based querying and can be integrated into ETL pipelines or data marketplaces that expose ontology-grounded data.

Limitations include the lack of a formal standard or broad tooling around dataonto, potential complexity in ontology engineering, and scalability challenges for very large datasets. Related concepts include ontology engineering, the semantic Web, and OBDA.