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Knowledge discovery in ontologies
Intelligent Data Analysis, 2012Ontologies allow us to represent knowledge and data in implicit and explicit ways. Implicit knowledge can be derived by means of several deductive logic-based processes. This paper introduces a new way for extracting implicit knowledge from ontologies by means of a link analysis of the T-box of the ontology integrated with a data mining step on the A ...
Furletti B., TURINI, FRANCO
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Knowledge Discovery in Databases
2019The huge amount of data, generated by daily-life data sources, represents a big opportunity for the development and advancement in several fields: scientific research, social life and industry. At the same time, analyzing these big repositories is a hard challenge, since the overload of information can overwhelm our capability of reading and ...
Massimo Guarascio +2 more
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Knowledge-Driven Lead Discovery
Mini-Reviews in Medicinal Chemistry, 2005Virtual screening encompasses several computational approaches which have proven valuable for identifying novel leads. These approaches rely on available information. Herein, we review recent successful applications of virtual screening. The extension of virtual screening methodologies to target families is also briefly discussed.
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Architectural knowledge discovery
ACM SIGSOFT Software Engineering Notes, 2006The need for a method for architectural knowledge discovery stems from the difficulty to find relevant architectural knowledge in the documentation that accompanies a software product. This difficulty arises in particular when the document set is very large, and has been expressed by auditors as a need for a "reading guide" during a case study we ...
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Knowledge Discovery and Knowledge Transfer
2016Reasoning-based methods have recently become popular since they overcome the knowledge-acquisition bottleneck during volume production and they can automatically generate an intelligent diagnostic system from existing resources [1, 2]. However, knowledge acquisition is a major problem for a reasoning-based method at the initial product ramp-up stage ...
Fangming Ye +3 more
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Knowledge Discovery in Neuroinformatics
2009Traditionally, the process of turning data into biomedical knowledge has involved “manual” meta-analyses of results reported in journals. Since the amount of scientific data produced in neuroscience today increases dramatically, the resultant expansion of the medical databases has created a significant potential for the design of ...
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2000
This Chapter reviews four major machine learning-based knowledge discovery paradigms, namely Rule Induction, Instance-Based Learning (or Nearest Neighbors), Neural Networks and Genetic Algorithms. For the sake of completeness, this Chapter also reviews On-Line Analytical Processing (OLAP).
Alex A. Freitas, Simon H. Lavington
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This Chapter reviews four major machine learning-based knowledge discovery paradigms, namely Rule Induction, Instance-Based Learning (or Nearest Neighbors), Neural Networks and Genetic Algorithms. For the sake of completeness, this Chapter also reviews On-Line Analytical Processing (OLAP).
Alex A. Freitas, Simon H. Lavington
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Artificial Intelligence Review, 2007
As knowledge discovery (KD) matures and enters the mainstream, there is an onus on the technology developers to provide the technology in a deployable, embeddable form. This transition from a stand-alone technology, in the control of the knowledgeable few, to a widely accessible and usable technology will require the development of standards.
Sarabjot Singh Anand +6 more
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As knowledge discovery (KD) matures and enters the mainstream, there is an onus on the technology developers to provide the technology in a deployable, embeddable form. This transition from a stand-alone technology, in the control of the knowledgeable few, to a widely accessible and usable technology will require the development of standards.
Sarabjot Singh Anand +6 more
openaire +1 more source