Lavrac Nada, Novak Petra Kralj, Mozetic Igor, Podpecan Vid, Motaln Helena, Petek Marko, Gruden Kristina
Jozef Stefan Institute, Jamova 39, Ljubljana, Slovenia.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5613-6. doi: 10.1109/IEMBS.2009.5333782.
A major challenge for next generation data mining systems is creative knowledge discovery from highly diverse and distributed data and knowledge sources. This paper presents an approach to information fusion and creative knowledge discovery from semantically annotated knowledge sources: by using ontology information as background knowledge for semantic subgroup discovery, rules are constructed that allow the expert to recognize gene groups that are differentially expressed in different types of tissues. The paper presents also current directions in creative knowledge discovery through bisociative data analysis, illustrated on a systems biology case study.
下一代数据挖掘系统面临的一个主要挑战是从高度多样化和分布式的数据及知识源中进行创造性的知识发现。本文提出了一种从语义标注的知识源进行信息融合和创造性知识发现的方法：通过将本体信息用作语义子组发现的背景知识，构建规则，使专家能够识别在不同类型组织中差异表达的基因组。本文还介绍了通过双联想数据分析进行创造性知识发现的当前方向，并以一个系统生物学案例研究进行了说明。
