OWL research at the University of Manchester

Joint research by members of the Information Management Group and the Bio-Health Informatics Group.

Research Overview

Ongoing projects

Start Title Project Description
2017 The Wearable Clinic: Connecting Health, Self and Care Grant. Contact: Bijan Parsia
20? Chiron: Contextually Intelligent Clinical Systems Contact: Bijan Parsia
2015 MCQ generation from OWL ontologies Generating MCQs is a time-consuming and error-prone process. Generating them automatically from existing knowledge bases would be desirable, but is an ongoing and largely onsolved problem. In a collaboration with Elsevier, we attempt to develop a novel approach for generating MCQs based on the OWL version of a large clinical knowledge base, EMMeT. Contact: Bijan Parsia
2017 D-WISE Data related to clinical trials is big, complex, and comes with massive amounts of constantly changing background knowledge. Accessing this data in a flexible, transparent, and reliable way is crucial for companies in life sciences and healthcare. Ontologies promise to capture rich background knowledge, and related Semantic Web (SW) technologies promise to provide the above-mentioned access to this data.

In this project, we explore the potential opportunities, cost and benefits of ontologies and SW technologies for capturing and accessing clinical trials data. We develop an OWL-based model to represent certain aspects of clinical trial data, build a demonstrator that allows a domain expert without OWL knowledge to specify a trial and search for trials, and a further demonstrator that translates clinical trial data between this model and SDTM. Contact: Uli Sattler

2016 NICE NICE provides national guidance and advice to improve health and social care, e.g., through clinical guidelines. Developing and maintaining these guidelines is a time-consuming and difficult task that is currently done manually by groups of health experts using a document-based format. NICE are adopting Semantic Web Technologies for a more maintainable representation of clinical guidelines.

Semantic Web Technologies provide the means to capture knowledge about a domain in machine-processable way and the usage of this knowledge in applications. Recent research on ontology languages and tools has produced results that could be used to support the task of representing and maintaining clinical guidelines. This project aims to develop a demonstrator consisting of a suitable part of the guideline ontology and a demonstrator applications highlighting the benefits of semantic web technology for the development and exploitation of guidelines. Contact: Uli Sattler

20* Snap-on . Contact: Sean Bechhofer
20* Music Ontology . Contact: Sean Bechhofer
20* Zubito . Contact: Uli Sattler
20* Mekon/Hobo Mekon and Hobo are Java software frameworks for building ontology-driven applications. The software is open source and available via the GitHub project: https://github.com/colin-puleston/mekon-hobo. For further details see project wiki: https://github.com/colin-puleston/mekon-hobo/wiki. Contact: Colin Puleston
2017 Phylogenic trees and Cladograms in OWL: A case study on Dinosaurs We investigate how ontologies and reasoning can support the design and evaluation of phylogenic trees. In particular, we are interested into logically and conceptually sound models of clades, groups, and specimen, as well as their relationships (e.g., developsFrom) and properties (e.g., genotypical or phenotypical features). Contact: Uli Sattler

Ongoing PhD theses

Expected Title Student Description
2018 Multiple choice question generation: difficulty modelling Ghader Kurdi Automatic question generation (AQG) is a process that involves using computer technology to generate questions. One of the limitations of the AQG techniques is the simplicity (or even the lack) of difficulty models. Sources underlying the difficulty of questions need to be identified and integrated into the generation process. Thus, the formulation of a theory behind an intelligent automatic question generator that is capable of both generating question of varied difficulty and predicting their difficulty accurately is at the heart of my research project.

Previous PhD theses

Finished Title Student Description
2017 General Terminology Induction in Description Logics Slava Sazonau Since manual engineering of TBoxes in Description Logics and OWL is a difficult, time-consuming task, automated acquisition of them from data (ABoxes) has attracted research attention and is usually called Ontology Learning. This project investigates the problem from general principles and formulates it as General Terminology Induction aiming at acquiring general, expressive TBox axioms from data (given ABox) while taking available background knowledge (given TBox) into account. We design a semantically sound approach and implement it in a system called DL-Miner.
2016 Capturing Temporal Aspects of Bio-Health Ontologies Jared Leo Description.
2016 Module-based classification of OWL ontologies Nicolas Matentzoglu Description.
2015 Ontology-based multiple-choice question Generation Tahani Alsubait Description.
2014 Impact Analysis in Description Logic Ontologies Rafael Gonçalves Description.
2013 The Modular Structure of an Ontology: Atomic Decomposition and its Applications Chiara del Vescovo Description.
2013 The Justificatory Structure of OWL Ontologies Samantha Bail Description
2011 Practical Reasoning in Probabilistic Description Logic Pavel Klinov Description.
2011 Justification Based Explanation in Ontologies Matthew Horridge Description.
2011 Nonmonotonic Reasoning with Description Logics Peihong Ke Description.