Context as history: the cat’s cradle network
Gammack, J., Pigott, D. and Hobbs, V.J. (2002) Context as history: the cat’s cradle network. In: Knowledge Management in Context: Australian Conference for Knowledge Management & Intelligent Decision Support (ACKMIDS 2001), 10 - 11 December 2001, Melbourne.
|PDF - Authors' Version |
The issue of context has long troubled the endeavour of modelling and representing knowledge, and today is central to the effective management of digital assets. The deep semantics associated with usage of particular digitised artefacts must be represented to a degree sufficient to avoid intentional or unintentional misrepresentation through the repurposing enabled by digital technologies. Any representational issue of relating context to digital objects involves linking in some form, and the assumptions about how linking occurs determines implementation strategy. We describe four models that may be distinguished in approaching implementations of linked documents: the causal, the associative or attributive, the purposive (usage based) and the communal, and contend that most, if not all, current knowledge modelling schemes can in principle be shown to reduce to a formal equivalence of one or other of these. We argue that the communal model is the candidate most likely to provide satisfactory contextualisation, and that an historically contextualised network of communally grounded linkages provides the most adequate mechanism for modelling knowledge provenance. We propose the concept of the ‘cat’s cradle network’ as a model for such an historicised contextualising network.
|Publication Type:||Conference Paper|
|Murdoch Affiliation:||School of Information Technology|
|Notes:||In Burstein, F., and Linger, H. (Eds): Knowledge Management in Context: Proceedings of the Australian Conference on Knowledge Management and Intelligent Decision Support, 10-11 December 2001, Melbourne, Australia. Australian Scholarly Publishing, Melbourne. pp. 122-134. Presented (Gammack) at ACKMIDS 2001|
|Item Control Page|
Downloads per month over past year