Knowledge Discovery - Part 1

author: Marko Grobelnik, Artificial Intelligence Laboratory, Jožef Stefan Institute
published: Feb. 25, 2007,   recorded: July 2005,   views: 95
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The basic idea of Knowledge discovery is to let a computer search for knowledge whereas the humans give just broad directions about where and how to search. Surprisingly, it is often the case that already relatively simple techniques are able to uncover useful hidden truth beneath the surface of the known facts and relationships. Knowledge discovery could be defined as a research area with several subfields with the most representative Machine Learning and Data Mining (Mitchell, 1997; Fayyad et al., 1996; Witten and Frank, 1999; Hand et al., 2001) and Data bases. Different real-life problems have been successfully addressed using Knowledge discovery methods including Data mining and Decision support (Mladenic et al., 2003; Mladenic and Lavrac, 2003). Semantic Web (Barnes-Lee and Fischetti, 1999) on the other hand, can be seen as mainly dealing with integration of many, already existing ideas and technologies with the specific focus of upgrading the existing nature of web-based information systems to a more “semantic” oriented nature. In this context Semantic Web could be viewed as a frontier of Knowledge Management with some emphasis on web-based applications. There are several dimensions along which Knowledge Discovery (KD) can bring important contributions to Semantic Web. Since KD techniques are mainly about discovering structure in the data, this can serve as one of the key mechanisms for structuring knowledge into an ontological structure being further used in Knowledge management process. An interesting aspect is that data and corresponding semantic structures change in time. As the consequence, we need to be able to adapt ontologies that are modeling the data accordingly. Sub-field of KD called “stream mining” deals with these kinds of problems. It is also important to point out that scalability is one of the central issues in KD, especially in the sub-areas such as Data mining where one needs to be able to deal with real-life datasets of the terra-byte sizes. Semantic Web is ultimately concerned with real-life data on the web which have exponential growth.

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