Abstraction in Ontology-based Data Management

Effectively documenting data services is a crucial issue in any organization, not only for governing data but also for interoperation purposes. Indeed, in order to fully realize the promises and benefits of a data-driven society, data-driven approaches need to be resilient, transparent, and fully accountable.

This book, Abstraction in Ontology-based Data Management, proposes a new approach to automatically associating formal semantic description to data services, thus bringing them into compliance with the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles. The approach is founded on the Ontology-based Data Management (OBDM) paradigm, in which a domain ontology is used to provide a high-level semantic layer mapped to the source schema of an organization containing data, thus abstracting from the technical details of the data layer implementation. A formal framework for a novel reasoning task in OBDM, called Abstraction, is introduced in which a data service is assumed to be expressed as a query over the source schema, and the aim is to derive a query over the ontology that semantically describes the given data service best with respect to the underlying OBDM specification. In a general scenario that uses the most popular languages in the OBDM literature, an in-depth complexity analysis of two computational problems associated with the framework is carried out. Also investigated is the problem of expressing abstractions in a non-monotonic query language as well as the impact of adding inequalities. Regarding the latter, the problem of answering queries with inequalities over lightweight ontologies is first studied. Lastly, the author illustrates how the achieved results contribute to new results in the Semantic Web context and in the Relational Database theory.

The book will be of interest to all those engaged in Artificial Intelligence and Data Management.

Author: Cima, G.
Pages: 268
Binding: softcover
Volume 348 of Frontiers in Artificial Intelligence and Applications
ISBN print: 978-1-64368-258-7
ISBN online: 978-1-64368-259-4