Artificial Intelligence for Engineering Design, Analysis and Manufacturing
- Artificial Intelligence for Engineering Design, Analysis and Manufacturing / Volume 23 / Special Issue 04 / November 2009, pp 427-441
- Copyright © Cambridge University Press 2009
- DOI: http://dx.doi.org/10.1017/S089006040900016X (About DOI), Published online: 17 April 2009
Research Article
From data to knowledge mining
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Abstract
Most past approaches to data mining have been based on association rules. However, the simple application of association rules usually only changes the user's problem from dealing with millions of data points to dealing with thousands of rules. Although this may somewhat reduce the scale of the problem, it is not a completely satisfactory solution. This paper presents a new data mining technique, called knowledge cohesion (KC), which takes into account a domain ontology and the user's interest in exploring certain data sets to extract knowledge, in the form of semantic nets, from large data sets. The KC method has been successfully applied to mine causal relations from oil platform accident reports. In a comparison with association rule techniques for the same domain, KC has shown a significant improvement in the extraction of relevant knowledge, using processing complexity and knowledge manageability as the evaluation criteria.
(Received November 14 2007)
(Accepted November 27 2008)
KeywordsData Mining; Knowledge Cohesion; Ontology; Sense Making; Text Mining
Footnotes
Reprint requests to: Ana Cristina Bicharra Garcia, Rua Passo da Pátria, 156, Bloco E, sl. 326, São Domingos Niterói, RJ, CEP 24210-240, Brazil. E-mail: bicharra@ic.uff.br
