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PhD Topics

This page contains a list of research topics in which the Knowledge Management Research Centre at the Faculty of Computing actively engages. This list is by no means meant to be exhaustive but reflects local research interests and culture. Applicants are requested to match their research interests with topics provided in the list below and to identify those matching topics on their application form. The topics are organised by research domain.

  • Knowledge Management
  • Management of Information Systems
  • Content-Based Indexing and Retrieval of Text Documents - Web Search engines

Knowledge Management
KMRC members have gained an extensive experience in both aspects of Knowledge management: the explicit knowledge and the tacit knowledge. PhD research topics in the management of explicit knowledge (the organisation information systems) evolve around research themes such as capturing, organizing, and retrieving information, application of neural network and data mining for knowledge discovery, text clustering, information retrieval (ARISTOTLE Project), integrated databases (OSCON Project), documents management (CONDOR Project) and management of information systems (COMMIT Project). PhD research topics in the management of tacit knowledge (Knowledge which is inextricably bound up with human cognition) evolves around developing framework and models and innovative methods and tools to address the fundamental research questions related toknowledge elicitation and memorisation, knowledge creation, knowledge sharing and knowledge transfer between different department of an organisation. There are also important PhD research questions on developing methods and techniques to support the transformation within and between tacit and explicit forms of knowledge. PhD topics on tacit knowledge could be:

  • Investigating the human and social factors at play in the production and use of knowledge
  • Investigating the development of a framework and methodology for Knowledge Creation in organisations,
  • Analysing the factors that lead to efficient knowledge creation, sharing and mobilisation in an organisation
  • Develop a knowledge fusion framework to improve knowledge flows in an enterprise
  • Investigating ways of emulating the knowledge flow from the supply side to improving it from demand side
  • Developing a meaningful knowledge memory for an organisation
  • Identify classification schemes to build an organizational taxonomy for organizing content and aiding Navigation
  • Finding out the barriers for knowledge sharing in organisation
  • Investigating the impact of knowledge transfer between subsidiaries and parent company
  • Developing a framework and models for efficient Knowledge Transfer

Management of Information Systems - Proactive Information Systems
The PhD research topics proposed in the domain of management of information systems aims at addressing the debate amongst academics and practitioners as to exactly how successful information systems can be developed and how they can be managed to keep up with the dynamic changes of organisations.One school of thought prescribes a mechanistic approach with detailed methods and tools, while others argue that such an approach is in no way grounded in real-world experience and recommend a new proactive approach for developing and managing information systems. There are two major research question in managing information systems: (i) How to develop and maintain information systems (and information itself) to keep up with organisation rapid changes and help the users locate potentially relevant resources (data, information and knowledge) based on their current tasks or interests and (ii) what are the factors and criteria which will be used to assess existing information system (and the information itself) needs to be upgraded or changed to reflect the rapidly evolving nature of computer-based information systems development. Some PhD research could be undertaken to:

  • Investigate the extension of theory and practice of designing and managing proactive information systems through the analysis of the social (human aspects) and technical factors (new computer based information systems development) that relate to them.
  • Study the interaction between groups of people, collections of machines and sets of procedures to develop a framework and methods/best practice which will be able:
  • Identify the social factors (user profile, culture, etc…) and technical factors (new techniques for IS development and management) that have impact on the development and the management of proactive information systems,
  • Identify appropriate approaches in organising and maintaining information (removing unnecessary information) and also efficient management of information systems that allows the right (non redundant and well organised) information to be communicated to the right people at the right time
  • Developing benchmarking test for assessing information systems (and information itself) accuracy and adaptability at both a strategic and an operational level to keep up with rapid changes in both organisation and people needs and also new techniques for information systems development and management.

Content-Based Indexing and retrieval of Text Documents -Web Search engines
The amount of information available on the Internet is currently growing at an incredible rate. However, the lack of efficient indexing is still a major barrier to effective information retrieval on the web. A lot of research has gone into developing retrieval systems on the web. Despite all that, using current indexing techniques, it has been reliably estimated that on average only 30% of the returned items are relevant to the user’s need, and that 70% of all relevant items in the collection are never returned. These results are far from ideal considering the user is still presented with thousands of documents pertaining to a keyword query in milliseconds. Existing indexing techniques, mainly used by search engines, are keyword based. In other words, each document is represented by a set of meaningful terms (also called descriptors or keywords) that are believed to express its content. The major drawback to keyword based methods is that they only use a small amount of the information associated with a document as the basis for relevance decisions. As a consequence, irrelevant information that uses a certain word in a different context might be retrieved or information where different words about the desired content are used might be missed. To achieve better performance, more semantic information about the documents needs to be captured. Some attempts at improving the traditional techniques using Natural Language Processing, logic and document clustering have offered some improvements. Some of the KMRC members have supervised PhD students who worked on the development new content-based indexing and retrieval algorithms (such as RST-Index) which uses computational and linguistic techniques such as Rhetorical Structure Theory (RST) and Natural Language Understanding (NLU) and there is still a need for PhD students to extend the research on content-based indexing and retrieval to improve what has been achieved so far.

 


 
 
  Page last updated : : 25 Feb 2011