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Knowledge Discovery for Ontology Construction
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2.1. INTRODUCTION
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We can observe that the focus of modern information systems is moving from data-processing towards concept-processing , meaning that the basic unit of processing is less and less is the atomic piece of data and is becoming more a semantic concept which carries an interpretation and exists in a context with other concepts. As mentioned in the previous chapter, an ontology is a structure capturing semantic knowledge about a certain domain by describing relevant concepts and relations between them. Knowledge Discovery (KD) is a research area developing techniques that enable computers to discover novel and interesting information from raw data. Usually the initial output from KD is further re ned via an iterative process with a human in the loop in order to get knowledge out of the data. With the development of methods for semi-automatic processing of complex data it is becoming possible to extract hidden and useful pieces of knowledge which can be further used for different purpose including semi-automatic ontology construction. As ontologies are taking a signi cant role in the Semantic Web, we address the problem of semi-automatic ontology construction supported by Knowledge Discovery. This chapter presents several approaches from Knowledge Discovery that we envision as useful for the Semantic Web and in particular for semi-automatic ontology construction. In that light, we propose to decompose the semi-automatic ontology construction process
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Semantic Web Technologies: Trends and Research in Ontology-based Systems John Davies, Rudi Studer, Paul Warren # 2006 John Wiley & Sons, Ltd
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KNOWLEDGE DISCOVERY FOR ONTOLOGY CONSTRUCTION
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into several phases. Several scenarios of the ontology learning phase are identi ed based on different assumptions regarding the provided input data. We outline some ideas how the de ned scenarios can be addressed by different Knowledge Discovery approaches. The rest of this is structured as follows. Section 2.2 provides a brief description of Knowledge Discovery. Section 2.3 gives a de nition of the term ontology. Section 2.4 describes the problem of semi-automatic ontology construction. Section 2.5 describes the proposed methodology for semi-automatic ontology construction where the whole process is decomposed into several phases. Section 2.6 describes several Knowledge Discovery methods in the context of the semi-automatic ontology construction phases de ned in Section 2.5. Section 2.7 gives a brief overview of the existing work in the area of semi-automatic ontology construction. Section 2.8 concludes the with discussion.
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2.2. KNOWLEDGE DISCOVERY
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The main goal of Knowledge Discovery is to nd useful pieces of knowledge within the data with little or no human involvement. There are several de nitions of Knowledge Discovery and here we cite just one of them: Knowledge Discovery is a process which aims at the extraction of interesting (nontrivial, implicit, previously unknown and potentially useful) information from data in large databases (Fayad et al., 1996). In Knowledge Discovery there has been recently an increased interest for learning and discovery in unstructured and semi-structured domains such as text (Text Mining), web (Web Mining), graphs/networks (Link Analysis), learning models in relational/ rst-order form (Relational Data Mining), analyzing data streams (Stream Mining), etc. In these we see a great potential for addressing the task of semi-automatic ontology construction. Knowledge Discovery can be seen as a research area closely connected to the following research areas: Computational Learning Theory with a focus on mainly theoretical questions about learnability, computability, design and analysis of learning algorithms; Machine Learning (Mitchell, 1997), where the main questions are how to perform automated learning on different kinds of data and especially with different representation languages for representing learned concepts; Data-Mining (Fayyad et al., 1996; Witten and Frank, 1999; Hand et al., 2001), being rather applied area with the main questions on how to use learning techniques on large-scale real-life data; Statistics and statistical learning (Hastie et al., 2001) contributing techniques for data analysis (Duda et al., 2000) in general.
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