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Details of Grant 

EPSRC Reference: EP/S032207/1
Title: quantMD: Ontology-Based Management of Many-Dimensional Quantitative Data
Principal Investigator: Wolter, Professor F
Other Investigators:
Zimmermann, Dr M Konev, Professor B
Researcher Co-Investigators:
Project Partners:
Free University of Bozen-Bolzano Pilsudski Institute of America Siemens AG (International)
SIRIS Academic SL SIRIUS Centre for Scalable Data Access
Department: Computer Science
Organisation: University of Liverpool
Scheme: Standard Research
Starts: 01 June 2019 Ends: 31 May 2022 Value (£): 402,689
EPSRC Research Topic Classifications:
Information & Knowledge Mgmt
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
EP/S032282/1
Panel History:
Panel DatePanel NameOutcome
05 Mar 2019 EPSRC ICT Prioritisation Panel March 2019 Announced
Summary on Grant Application Form
Ontology-based data management (OBDM) is a technology that has been developed over the past decade with the aim of facilitating access to various types of data sources. In general, ontologies provide a formal model and vocabulary for a domain of interest. In OBDM, the role of ontologies is threefold: to integrate distributed and heterogeneous data sources, enrich incomplete data with background knowledge, and provide a user-friendly language for querying.

To illustrate, in an energy company the traditional workflow for geologists to find answers to their information needs is to either execute pre-defined queries covering parts of the needs over their databases and then integrate the results manually, which is onerous and error-prone, or to ask the IT department to construct custom SQL queries, which may takes days or even weeks. OBDM reduces the time for finding answers to minutes by allowing the geologists to formulate their queries in natural-language terms and then run these queries via the OBDM tools over their databases.

Thus, by bringing together knowledge representation and database technologies, OBDM has the potential to transform information systems by allowing domain experts to query complex and distributed data efficiently without the help of database professionals.

This project addresses the main bottleneck in the way to realise this potential: so far, OBDM has been developed primarily for access to purely qualitative and one-dimensional data, but nowadays data is mostly numerical, many-dimensional, often temporal, and user information needs usually involve quantitative analysis. Thus, quantitative queries such as "find all UK-sponsored research institutions in Europe whose total triennial financial contributions from UK-based private companies exceeds euro 10M" are not supported at all by existing OBDM tools. Moreover, because of the so-called open world assumption made in OBDM, developing the theory and practical tools for dealing with such queries is extremely challenging.

The aim of this project is to develop a novel OBDM framework for querying and analysing many-dimensional numerical data. To address the challenges, we bring together techniques from databases, knowledge representation, and formal methods, in particular temporal and modal logics, and develop these further. We will develop a theoretical framework for querying such data, develop tools for using this framework in practice, and test our tools with partners from industry and the public sector.

Key Findings
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Potential use in non-academic contexts
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Organisation Website: http://www.liv.ac.uk