In information science, ontology is a formal representation of knowledge as a set of concepts within a domain, and the relationships between those concepts. It is used to reason about the entities within that domain, and may be used to describe the domain. (Wikipedia, 2011) This research takes two case study ICT applications in engineering and medicine, and evaluates the applications and supporting ontology to identify the main requirements for ontology in ICT systems. A study of existing ontology engineering methodology revealed difficulties in generating sufficient breadth and depth in domain concepts that contain rich internal relationships. These restrictions usually arise because of a heavy dependence on human experts in these methodologies. This research has developed a novel ontology engineering methodology – SEA, which economically, quickly and reliably generates ontology for domains that can provide the breadth and depth of coverage required for automated ICT systems. Normally SEA only requires three pairs of keywords from a domain expert. Through an automated snowballing mechanism that retrieves semantically related terms from the Internet, ontology can be generated relatively quickly. This mechanism also enhances and enriches the binary relationships in the generated ontology to form a network structure, rather than a traditional hierarchy structure. The network structure can then be analysed through a series of statistical network analysis methods. These enable concept investigation to be undertaken from multiple perspectives, with fuzzy matching and enhanced reasoning through directional weight-specified relationships. The SEA methodology was used to derive medical and engineering ontology for two existing ICT applications. The derived ontology was quicker to generate, relied less on expert contribution, and provided richer internal relationships. The methodology potentially has the flexibility and utility to be of benefit in a wide range of applications. SEA also exhibits "reliability" and "generalisability" as an ontology engineering methodology. It appears to have application potential in areas such as machine translation, semantic tagging and knowledge discovery. Future work needs to confirm its potential for generating ontology in other domains, and to assess its operation in semantic tagging and knowledge discovery.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:632866 |
| Date | January 2011 |
| Creators | Ma, Xiao |
| Publisher | University of Warwick |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://wrap.warwick.ac.uk/63881/ |
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