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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.<\/jats:p>","DOI":"10.1017\/s089006040900016x","type":"journal-article","created":{"date-parts":[[2009,4,17]],"date-time":"2009-04-17T04:55:47Z","timestamp":1239944147000},"page":"427-441","source":"Crossref","is-referenced-by-count":8,"title":["From data to knowledge mining"],"prefix":"10.1017","volume":"23","author":[{"given":"Ana Cristina","family":"Bicharra Garcia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Inhauma","family":"Ferraz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adriana S.","family":"Vivacqua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2009,4,17]]},"reference":[{"key":"S089006040900016X_ref17","doi-asserted-by":"publisher","DOI":"10.1016\/S0045-7949(02)00017-2"},{"key":"S089006040900016X_ref6","first-page":"1","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Fayyad","year":"1996"},{"key":"S089006040900016X_ref11","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-48751-4_17"},{"key":"S089006040900016X_ref29","doi-asserted-by":"publisher","DOI":"10.1016\/S0045-7949(02)00344-9"},{"key":"S089006040900016X_ref14","doi-asserted-by":"publisher","DOI":"10.1017\/S0890060407070199"},{"key":"S089006040900016X_ref7","doi-asserted-by":"publisher","DOI":"10.1006\/knac.1993.1008"},{"key":"S089006040900016X_ref25","unstructured":"Smith I.F.C. , Saitta S. , Ravindran S. , & Kripakaran P . 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A unifying semantic distance model for determining the similarity of attribute values. Proc. 26th Australasian Computer Science Conf., pp. 111\u2013118."},{"key":"S089006040900016X_ref27","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1017\/S0890060408000048","article-title":"Comparing mathematical and heuristic approaches for scientific data analysis","volume":"22","author":"Varde","year":"2007","journal-title":"Artificial Intelligence for Engineering Design, Analysis and Manufacturing"},{"key":"S089006040900016X_ref24","unstructured":"Sirim E. , Hendler J. , & Parsia B . (2003). Semi-automatic composition of web services using semantic description. Proc. Web Services: Modeling Architectures and Infrastructures Workshop in Conjunction With ICEIS 2003."},{"key":"S089006040900016X_ref5","unstructured":"Etzkorn L.H . (2006). Semantic metrics, conceptual metrics, and ontology metrics: an analysis of software quality using IR-based systems, potential applications and collaborations. Proc. Int. Conf. Software Maintenance, Philadelphia, PA."},{"key":"S089006040900016X_ref10","doi-asserted-by":"publisher","DOI":"10.1145\/1027092.1027094"},{"key":"S089006040900016X_ref19","unstructured":"Refrat R.M. , Gero J. , & Peng W . (2004). Using data mining for improving building life cycle. Proc. 8th Pacific\u2013Asia Conf. 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