
From data to knowledge mining
2009; Cambridge University Press; Volume: 23; Issue: 4 Linguagem: Inglês
10.1017/s089006040900016x
ISSN1469-1760
AutoresAna Cristina Bicharra García, Inhaúma Neves Ferraz, Adriana S. Vivacqua,
Tópico(s)Semantic Web and Ontologies
ResumoAbstract 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.
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