# ontology-based data integration > data integration approach **Wikidata**: [Q7095047](https://www.wikidata.org/wiki/Q7095047) **Wikipedia**: [English](https://en.wikipedia.org/wiki/Ontology-based_data_integration) **Source**: https://4ort.xyz/entity/ontology-based-data-integration ## Summary Ontology-based data integration is a specific approach to data integration that utilizes ontologies to unify data from different sources. It is classified as a subclass of data integration and operates within the domain of ontology-based data access. This approach employs semantic techniques to provide a unified view of diverse data, often enabling the querying of relational databases as Virtual RDF Knowledge Graphs. ## Key Facts * **Classification:** It is a distinct subclass of **data integration**, which is defined as the process of combining data from different sources to provide a unified view. * **Domain:** It falls under the category of **ontology-based data access** (OBDA), a range of semantic techniques and algorithms developed to facilitate access to various data source types. * **Freebase ID:** /m/02rf5_3 * **Microsoft Academic ID:** 22550185 (discontinued service) * **Wikipedia Presence:** It has a dedicated article titled "Ontology-based data integration" on the English Wikipedia. * **Associated Platform:** The approach is closely related to **Ontop**, a platform used to query relational databases as Virtual RDF Knowledge Graphs using SPARQL. ## FAQs ### Q: How does ontology-based data integration relate to standard data integration? A: Ontology-based data integration is a specialized subclass of data integration. While general data integration focuses on combining sources for a unified view, the ontology-based approach specifically uses semantic techniques and ontologies to structure and access this data. ### Q: What is the role of ontology-based data access (OBDA) in this method? A: Ontology-based data integration utilizes the principles of ontology-based data access (OBDA). OBDA provides the semantic techniques, algorithms, and systems required to facilitate access to the various data sources being integrated. ### Q: Are there specific tools associated with this integration approach? A: Yes, **Ontop** is a relevant platform associated with this approach. It allows users to query relational databases as Virtual RDF Knowledge Graphs using SPARQL, serving as a practical application of these integration techniques. ## Why It Matters Ontology-based data integration matters because it solves the fundamental challenge of unifying data that resides in fundamentally different formats or structures. By leveraging ontologies—a shared vocabulary of concepts—this approach allows organizations to treat disparate data sources as a single, coherent information space without needing to physically duplicate or alter the original data. This methodology is significant in the field of data science and knowledge management because it bridges the gap between traditional relational databases and the semantic web. Through related technologies like Ontop, it enables the use of SPARQL (a semantic query language) over standard relational databases, transforming them into Virtual RDF Knowledge Graphs. This allows for more flexible, intelligent querying and "data interoperability" between systems that would otherwise be unable to communicate effectively. It represents a shift from syntactic integration (merging file formats) to semantic integration (merging meanings), ensuring that the combined data retains its context and usability. ## Notable For * Being a **subclass of data integration**, representing a specialized semantic methodology within the broader field. * Utilizing **ontology-based data access (OBDA)** techniques to facilitate the connection of various data sources. * Enabling the transformation of standard **relational databases into Virtual RDF Knowledge Graphs**. * Supporting **SPARQL** querying capabilities over integrated relational data. * Providing the theoretical and practical framework for platforms like **Ontop**. ## Body ### Definition and Classification Ontology-based data integration is formally classified as a **data integration approach**. It is hierarchically positioned as a subclass of **data integration**, a class defined by the activity of combining data from different sources and providing a unified view of them. This approach is intrinsically linked to **ontology-based data access** (OBDA). OBDA is defined as a range of semantic techniques, algorithms, and systems developed to facilitate access to various types of data sources. Consequently, ontology-based data integration can be viewed as the application of OBDA principles to the specific problem of unifying disparate data. ### Technical Implementation and Tools A primary example of the technology enabling ontology-based data integration is the **Ontop** platform. Ontop is a tool that allows for the querying of relational databases as Virtual RDF Knowledge Graphs. * **Virtual RDF Graphs:** Instead of moving data into a new storage system, the data remains in the original relational database but is presented to the user as an RDF graph. * **SPARQL Support:** The platform utilizes SPARQL, a standard query language for RDF data, allowing for complex semantic queries across the integrated data sources. ### Identifiers and Academic Context The concept is tracked across various knowledge bases and academic repositories: * **Wikidata:** Lists the entity with a sitelink count of 1, pointing to the English Wikipedia entry. * **Freebase:** Identified by the ID `/m/02rf5_3`. * **Microsoft Academic:** Assigned the ID `22550185` (note: this service has been discontinued). ## References 1. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)