Timbr is the intelligent semantic layer based on SQL knowledge graph technology to integrate, model and deliver data products. It enables integration of data sources by connecting them to a semantic data model powered with relationships.
1. Timbr semantic layer modeling consists in the automatic / semi-automatic / no-code / coded creation of a virtual ontology layer on top of an existing relational database schema. This virtual ontology captures semantic relationships, hierarchies, and inference rules that are not natively expressed in the relational model. The model can also be created from ontology templates and from ERDs.
2. The virtual ontology is defined using Ontology Definition Language (ODL), a SQL language extension. This allows declaring concepts, properties, inheritance, and mapping them to the underlying relational tables.
3. The virtual ontology is presented to users/applications as different relational views, each capturing certain ontological capabilities like hierarchy, inheritance, and graph traversal. These views appear as normal SQL tables that can be queried using standard SQL.
4. Timbr makes it fast and easy to connect any datastore, and securely share what you’ve built with other users – all in one place.
5. When an SQL query is made against one of these ontological views, their system rewrites the query to extract the semantically inferred data from the underlying relational tables. This is done by parsing the ODL definitions and applying corresponding relational operations like joins and unions.
6. As a result, users can leverage ontology-like semantic querying capabilities while still working entirely within the familiar SQL and relational database paradigm. The semantic mappings allow extracting knowledge that is implicit in the relational data but hard to query directly.
7. The modeled data can be accessed in (no JOINs) SQL, Spark, Phyton, Scala, Java and R, or can be consumed with business intelligence tools via ODBC/JDBC, data science tools and web applications via REST.
8. Modeled data can also be graphically explored as a network and can also be analyzed with a library of graph algorithms run with simple SQL queries.
Timbr bridges relational databases with semantic technologies, implementing SQL ontologies that provide the same power of semantic web ontologies as defined in OWL, while being implemented and queried using just SQL without requiring a separate graph database or SPARQL-like query language.
