ONTOCUBE: efficient ontology extraction using OLAP cubes

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, we 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. The solution was implemented within a relational database system (DBMS). In our experiments, we show how all the proposed discrimination measures (frequency, correlation, lift) affect the resulting classes. We also show a sample ontology result and the accuracy of finding true classes. Finally, we show the performance breakdown of our algorithm.