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Unformatted text preview: T.S. Dillon et al. (Eds.): Advances in Web Semantics I, LNCS 4891, pp. 130175, 2008. IFIP International Federation for Information Processing 2008 Extraction Process Specification for Materialized Ontology Views Carlo Wouters 1 , Tharam S. Dillon 2 , Wenny Rahayu 1 , Robert Meersman 3 , and Elizabeth Chang 2 1 Department of Computer Science and Computer Engineering La Trobe University, Bundoora, Victoria 3086, Australia {cewouter,wenny}@cs.latrobe.edu.au 2 Digital Ecosystems and Business Intelligence Institute, Curtin University of Technology, Perth, Australia tharam.dillon@cbs.curtin.edu.au, elizabeth.chang@cbs.curtin.edu.au 3 STARLab, Department of Computer Science Vrije Universiteit Brussel, Brussel, 1050, Belgium Robert.Meersman@vub.ac.be Abstract. The success of the semantic web relies heavily on ontologies. How- ever, using ontologies for this specific area poses a number of new problems. One of these problems, extracting a high quality ontology from a given base on- tology, is currently receiving increasing attention. Areas such as versioning, dis- tribution and maintenance of ontologies often involve this problem. Here, a formalism is presented that enables grouping ontology extraction requirements into different categories, called optimization schemes. These optimization schemes provide a way to introduce quality in the extraction process. An over- view of the formalism is discussed, as well as a demonstration of several exam- ple optimization schemes. Each of these optimization schemes meets a certain requirement, and consists of rules and algorithms. Examples of how the formal- ism is deployed to reach a high-quality result, called a materialized ontology view, are covered. The presented methodology provides a foundation for further developments, and shows the possibility of obtaining usable ontologies in a highly automated way. ACM Subject Descriptors (98): H.3.5 [INFORMATION STORAGE AND RETRIEVAL]: Web-based services; I.1.2 [SYMBOLIC AND ALGEBRAIC MANIPULATION]: Algorithms; I.2.4[ARTIFICIAL INTELLIGENCE]: Se- mantic networks --- Representation languages. Additional Keywords: Ontology Extraction. 1 Motivation In recent years, the unstructured storage of data, especially on the World Wide Web, and the difficulties experienced with retrieving relevant data with the existing search Extraction Process Specification for Materialized Ontology Views 131 engines, have triggered new research aimed at ameliorating information retrieval and storage. New ways of storing information meant for the Internet were developed, such as XML [W3C 1999], HTML a [Fensel, Decker et al. 1998], DTD and RDF. These lan- guages provide a tool to store the information in a structured way, but with that another problem arose; everyone was free to develop there own taxonomy of how they want to categorize their information, e.g. [Heflin, Hendler et al. 1999; Van Harmelen and Fensel 1999]. It is clear that widely accepted standards should be used as metadata to define 1999]....
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