| Data mining, also referred to as knowledge discovery in databases (KDD), is a process of finding new, interesting, previously unknown, potentially useful, and ultimately understandable patterns from very large volumes of data. Data mining is a discipline which brings together database systems, statistics, artificial intelligence, machine learning, parallel and distributed processing and visualization between other disciplines (Fayyad et al., 1996; Hand & Kamber, 2001; Hernadez Orallo et al., 2004).
Nowadays, one of the most important and challenging problems in data mining is the definition of the prior knowledge; this can be originated from the process or the domain. This contextual information may help select the appropriate information, features or techniques, decrease the space of hypothesis, represent the output in a most comprehensible way and improve the whole process.
Therefore we need a conceptual model to help represent to this knowledge. According to Gruber’s ontology definition—explicit formal specifications of the terms in the domain and relations among them (Gruber, 1993, 2002); we can represent the knowledge of knowledge discovery process and knowledge about domain. Principally, ontologies are used for communication (between machines and/or humans), automated reasoning, and representation and reuse of knowledge (Cimiano et al., 2004). As a result, ontological foundation is a precondition for efficient automated usage of knowledge discovery information.
Thus, we can perceive the relation between Ontologies and data mining in two manners:
• From ontologies to data mining, we are incorporating knowledge in the process through the use of ontologies, i.e. how the experts comprehend and carry out the analysis tasks. Representative applications are intelligent assistants for discover process (Bernstein et al., 2001, 2005), interpretation and validation of mined knowledge, Ontologies for resource and service description and knowledge Grids (Cannataro et al., 2003; Brezany et al., 2004).
• From data mining to Ontologies, we include domain knowledge in the input information or use the ontologies to represent the results. Therefore the analysis is done over these ontologies. The most characteristic applications are in medicine, biology and spatial data, such as gene representation, taxonomies, applications in geosciences, medical applications and specially in evolving domains (Langley, 2006; Gottgtroy et al., 2003, 2005; Bogorny et al., 2005).
When we can represent and include knowledge in the process through ontologies, we can transform data mining into knowledge mining. |
