Advanced information systems require the orchestration of many components, including ontologies or knowledge graphs, and e cient data management, in order to provide a means for better informed decision-making and to keep up with new requirements in organisational needs. A major question in delivering such systems, is which components to design and put together to create a `knowledge to data' pipeline, as each component and process has trade-o s, such as the computational complexity of the representation and query languages, which reasoning services that operate under an open or closed world, and maintainability. Such a combination of knowledge with data is named with various terms, including ontology-based data access (OBDA) [4], with (virtual) knowledge graphs gaining in popularity in research and industry (e.g., [12, 15]). OBDA has become synonymous with the approach of declaring a mapping layer between knowledge represented in an OWL le and data stored in a relational database whilst using query rewriting in answering conjunctive queries. That mapping layer is known to be costly both computationally [7] and in design and maintenance [10]. Also, database users may want to retain full SQL expressiveness when querying data, and remain within the closed world assumption they are more familiar with cf. the open world assumption in OBDA systems. In an attempt to avoid these issues, an \Abstract Relational Model" (ARM) with special object identi ers and a strict extension to SQL for path queries (SQLP) has been proposed [3] and experimentally shown to simplify queries [11]. This approach avoids the costly mapping layer through transformations and o ers more than full SQL, but the ARM is not an ontology. Put di erently this ARM+SQLP falls short of the knowledge layer. We aim to address this limitation of the ARM+SQLP option. To this end, we introduce a new knowledge-to-data architecture, KnowID: Knowledge-driven Information and Data access. It pulls together both recently proposed components of the knowledge layer and, to complete the pipeline, we add novel transformation rules between EER and the ARM, which enhances and adapts rules from the regular EER-to/from-Relational Model (RM) transformation with important additional expressiveness. KnowID's components and the addition to ARM+SQLP is visualised in Fig. 1. Regarding the four steps in the top block: 1) If the model is not in EER, one can convert it into EER by means of a metamodel or common core (e.g., [5, 9]); 2) If the EER diagram was not formalised
conceptual 1. Conversion to data model EER (if applicable) or application ontology C 2. Formalisation (if applicable) 3. Classification 4. Materialisation of deductions Knowledge and information
management ARM+SQLP
SQL Evaluation of result q1 over S+D
Data completion
EER diagram
transform Database transform schema(s) S
Data D
RM A’ transform
ARM A
Query request Q query formulation in SQLP, assisted
by A or C
q1 in SQLP
yet, one of the logic-based reconstructions may be used (e.g., [
KnowID's functionality is thus similar to OBDA systems [
We are currently implementing the EER$ARM transformation rules as a rst step toward concretely realising KnowID as a usable and scalable software system.