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Knowledge modelling, discovery and reasoning systems

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  • Full or part time
    Dr N Wiratunga
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

We are now accepting applications for a 3-year PhD studentship on Knowledge Modelling, Discovery and Reasoning for Materials Management platforms in the School of Computing Science and Digital Media, Robert Gordon University
Aim of the project
The aim of the project is to explore the hypothesis that similarity driven reasoning provides an interactive ontology population technique for semi-automatic knowledge graph construction.
Ontologies and knowledge graphs capture knowledge in the form of entities and relationships between them, and are being used in applications across domains such as geosciences, healthcare, retail, and manufacturing. In this industrial collaborative project, you will study the application of ontologies within reasoning platforms intended to address real-world challenges. In particular, you will study the consequences of using a network of ontologies and associated knowledge graph(s) that are being constructed in a semi-supervised manner, within reasoning methodologies such as case-based reasoning. The incorporation of ontologies within a real-world reasoning tasks bring several benefits, including: providing a formal definition of the data used by reasoning mechanisms; easing the sharing of that data by semantically describing it; and reducing the effort involved with integrating data from third parties by providing a data vocabulary/dictionary for describing the external data.
The project will also investigate related problems such as augmenting similarity metrics for case retrieval with a network of ontologies; testing the feasibility of the approach on real-world industry data; and synthesizing suitable language generation or automatically providing concise explanations for the reasoning.
Candidate’s profile
- A Masters degree with Distinction (or a First Class honours BSc in exceptional cases) from a UK university or equivalent overseas/professional qualification in computer science, artificial intelligence, mathematics, or other related subject.
- Strong analytical and object-oriented and/or functional programming and software engineering skills.
- Knowledge or experience in at least one field of symbolic AI (such as analogical reasoning, case-based reasoning, ontological modelling and reasoning, etc.).
- Good understanding or knowledge of at least two of the following:
o Information extraction
o NLP or NLG
o Ontologies, linked data, or knowledge graphs
o Case-based reasoning
o Knowledge modelling, discovery and reasoning
o Intelligent systems, design, development and integration.
- Excellent programming skills.
- Good command of English in writing and speaking.
- Ability to work collaboratively in a team, willingness to explore areas related to the target domain and excellent inter-personal skills
The studentship covers full time PhD tuition fees on an international or home basis, and a tax free stipend of GBP £14,777 per year for 3 years.
Applications
Applications should be emailed to Kate Lines at [Email Address Removed] by Wednesday 17th April 2019
The applications should consist of a covering letter or personal statement of interest, and CV. Further information such as passport details or transcripts may be requested during the short-listing stage. All shortlisted applicants will be interviewed before an offer is made.
General enquiries should be addressed to Kate at [Email Address Removed] and subject-matter enquires to Professor Nirmalie Wiratunga at [Email Address Removed].

Funding Notes

Masters degree with Distinction / First Class honours BSc in exceptional cases from a UK university or equivalent overseas/professional qualification in computer science, artificial intelligence, mathematics, other related subject.
Strong analytical and object-oriented and/or functional programming, software engineering skills
Knowledge/experience in at least one field of symbolic AI (eg analogical reasoning, case-based reasoning, ontological modelling and reasoning)
Good understanding or knowledge of at least two of the following:
Information extraction
NLP or NLG
Ontologies, linked data, knowledge graphs
Case-based reasoning
Knowledge modelling, discovery, reasoning
Intelligent systems, design, development, integration
Excellent programming skills
Good command of English
Collaborative team working



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