Data-driven or background knowledge ontology development

Emma Tonkin, Heather D Pfeiffer

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Abstract

The development of ontologies for various purposes is now a relatively commonplace process. A number of different approaches towards this aim are evident; empirical methodologies, giving rise to data-driven procedures or self-reflective (innate) methodologies, resulting in artifacts that are based on intellectual background understanding. In this paper, we compare and contrast these approaches through two practical examples, one from a descriptive metadata domain and one from the area of physical computing. Both examples are chosen from domains in which automated extraction of information is a significant use case for the resulting ontology. We identify a relationship within the ontology development process that allies empirical evidence and user judgement to develop user-centred ontologies, either on an individual or collaboratively-focused basis. A qualitative treatment of the characteristics of this type of 'language game' is identified as an ongoing research goal.
Original languageEnglish
Publication statusPublished - 23 Oct 2010
EventInternational Conference on Knowledge Management (ICKM) - Pittsburgh, USA United States
Duration: 22 Oct 201023 Oct 2010

Conference

ConferenceInternational Conference on Knowledge Management (ICKM)
CountryUSA United States
CityPittsburgh
Period22/10/1023/10/10

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    Tonkin, E., & Pfeiffer, H. D. (2010). Data-driven or background knowledge ontology development. Paper presented at International Conference on Knowledge Management (ICKM), Pittsburgh, USA United States.