With the massive deluges of data that several domains of study experience, referred to as Big Data, the need to efficiently process and analyze data has risen. Ontologies have been employed to structure these domains with the ultimate purpose of generating new knowledge about the respective domain. Currently, when creating or examining an ontology, the concepts of the domain are limited to being statically defined in relation to other concepts. This limitation on the concept's definition affects the reasoning process by omitting or not properly representing all information that may exist in the domain such as how the constantly evolving environment changes how the data can be understood.
The resulting tool of this research, Conto, allows for the interpretation of a concept as an abstract data type. When coupled to knowledge generation process, these interpretations allow the obtention of new knowledge that would traditionally be unobtainable. The different types of knowledge that can be obtained via the multiple interpretations are explored in this work using examples of ontologies.
Conto is a Protege plugin that uses Ontograf to
display the ontologies. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/18947 |
| Date | January 2016 |
| Creators | LeClair, Andrew |
| Contributors | Khedri, Ridha, Computing and Software |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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