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Ontologies are knowledge conceptualizations of a particular domain and are commonly represented with hierarchies. While final ontologies appear deceivingly simple on paper, building ontologies represents a time-consuming task that is normally performed by natural language processing techniques or schema matching. On the other hand, OLAP cubes are most commonly used during decision-making processes via the analysis of data summarizations. In this paper, the authors present a novel approach based on using OLAP cubes for ontology extraction. The resulting ontology is obtained through an analytical process of the summarized frequencies of keywords within a corpus.
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