Cyc: toward programs with common sense
Communications of the ACM
A translation approach to portable ontology specifications
Knowledge Acquisition - Special issue: Current issues in knowledge modeling
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
A Note on the Complexity of Dijkstra's Algorithm for Graphs with Weighted Vertices
IEEE Transactions on Computers
Data Mining and Knowledge Discovery
Case-Based Reasoning in Design
IEEE Expert: Intelligent Systems and Their Applications
Towards a Framework for Software Measurement Validation
IEEE Transactions on Software Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A unifying semantic distance model for determining the similarity of attribute values
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
Coupling and cohesion metrics for knowledge-based systems using frames and rules
ACM Transactions on Software Engineering and Methodology (TOSEM)
Coupling Metrics for Ontology-Based Systems
IEEE Software
Efficient mining of iterative patterns for software specification discovery
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Comparing mathematical and heuristic approaches for scientific data analysis
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Data mining techniques for improving the reliability of system identification
Advanced Engineering Informatics
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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.