OntoBuilder: Fully Automatic Extraction and Consolidation of Ontologies from Web Sources
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
DODDLE-OWL: Interactive Domain Ontology Development with Open Source Software in Java
IEICE - Transactions on Information and Systems
OLAP-based query recommendation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Statistical Model Computation with UDFs
IEEE Transactions on Knowledge and Data Engineering
Query processing on cubes mapped from ontologies to dimension hierarchies
Proceedings of the fifteenth international workshop on Data warehousing and OLAP
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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, 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.