iTract: Islands of Tractability in Ontology-Based Data Access

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15 years ago most data was structured, complete, and neatly organised in databases. This is no longer the case. Unstructured, incomplete, and heterogeneous data sets are proliferating at an enormous rate. This is most evident in the context of the World Wide Web, but also applies to scientific data, data in business and industry, data in healthcare and in many other areas. To make use of such data, traditional information systems based on standard database technologies are no longer sufficient. Ontology-based data access and management is a novel approach to address this challenge by introducing a semantic layer (ontology) that provides the user with a high-level unified view of the data as well as a vocabulary to access and query the data. Ontologies model application domains by providing machine readable definitions of terms and relationships between them. They are already used in numerous applications, for example, by the NHS: to enable communication between health professionals within the United Kingdom and worldwide, it is crucial that they use the same terminology; such a terminology is provided by the ontology SNOMED CT.Using ontologies to access data and thereby directly combining data and knowledge is a novel idea of the 21st century. First applications have demonstrated that ontology-based data access and management is indeed feasible and has the potential to revolutionise modern information systems. However, scalability of query answering with expressive ontology languages remains a big challenge, and it is the aim of this project to develop a new "island of tractability" approach to tackle it. Our approach links ontology-based data access with two well-established and successful areas of Computer Science: constraint satisfaction and Boolean circuit complexity. We aim to transfer proof methods, techniques, and methodologies from these two areas to ontology-based data access. This includes a non-uniform complexity analysis, where we aim to classify the complexity of answering ontology-mediated queries, which consist of an ontology and a standard database query. Based on this complexity analysis, we will develop uniformly efficient query answering algorithms for the identified islands of tractable ontology-mediated queries, and implement them in the ontology-based data access systems Ontop and Combo. We will apply our novel technology to case studies from oil and gas industry and healthcare.