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From data to knowledge mining

Published online by Cambridge University Press:  17 April 2009

Ana Cristina Bicharra Garcia ,
Inhauma Ferraz  and
Adriana S. Vivacqua
Ana Cristina Bicharra Garcia
Affiliation:
Laboratório de Documentação Ativa e Design Inteligente, Universidade Federal Fluminense, Fluminense, Brazil
Inhauma Ferraz
Affiliation:
Laboratório de Documentação Ativa e Design Inteligente, Universidade Federal Fluminense, Fluminense, Brazil
Adriana S. Vivacqua
Affiliation:
Laboratório de Documentação Ativa e Design Inteligente, Universidade Federal Fluminense, Fluminense, Brazil
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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.

Keywords

Data MiningKnowledge CohesionOntologySense MakingText Mining
Type
Research Article
Information
AI EDAM , Volume 23 , Issue 4: Problem Solving Methods: Past, Present, and Future , November 2009 , pp. 427 - 441
Copyright
Copyright © Cambridge University Press 2009

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