Abstract: Ontology Based Data Access (OBDA) refers to a range of techniques, algorithms and systems that can be used to deal with the heterogeneity of data that is common inside many organisations as well as in inter-organisational settings and more openly on the Web. In OBDA, ontologies are used to provide a global view over multiple local datasets; and mappings are commonly used to describe the relationships between such global and local schemas. Since its inception, this area has evolved in several directions. Initially, the focus was on the translation of original sources into a global schema, and its materialisation, including non-OBDA approaches such as the use of Extract Transform Load (ETL) workflows in data warehouses and, more recently, in data lakes. Then OBDA-based query translation techniques, relying on mappings, were proposed, with the aim of removing the need for materialisation, something especially useful for very dynamic data sources. We think that we are now witnessing the emergence of a new generation of OBDA approaches. It is driven by the fact that a new set of declarative mapping languages, most of which stem from the W3C Recommendation R2RML for Relational Databases (RDB), are being created. In this vision paper, we enumerate the reasons why new mapping languages are being introduced. We discuss why it may be relevant to work on translations among them, so as to benefit from the engines associated to each of them whenever one language and/or engine is more suitable than another. We discuss the emerging concept of “mapping translation”, the basis for this new generation of OBDA, together with some of its desirable properties: information preservation and query result preservation. We show several scenarios where mapping translation can be or is being already applied, even though this term has not necessarily been used in existing literature.
Keywords: OBDA, data translation, query translation, mapping translation
DOI: 10.3233/SW-190384
Journal: Semantic Web, vol. 11, no. 1, pp. 153-160, 2020
