Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
DBMS Research at a Crossroads: The Vienna Update
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
Using Relational Database to Build OWL Ontology from XML Data Sources
CISW '07 Proceedings of the 2007 International Conference on Computational Intelligence and Security Workshops
Mining the Content of Relational Databases to Learn Ontologies with Deeper Taxonomies
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Mapping ER Schemas to OWL Ontologies
ICSC '09 Proceedings of the 2009 IEEE International Conference on Semantic Computing
Improving inference through conceptual clustering
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
On how to perform a gold standard based evaluation of ontology learning
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Structure inference for linked data sources using clustering
Proceedings of the Joint EDBT/ICDT 2013 Workshops
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Since most bio-medical Linked Data Sets are simply extracted from the Relational database, lots of them are lack of ontology or concept hierarchy structure for user better understanding the data sets. This problem also limited usage of bio-medical Linked Data Sets. To resolve the problem, this paper introduced a method to dynamically generate the concept hierarchy from the Linked Data Sets. Based on the hierarchical clustering algorithm, we applied Vector Space Model(VSM) and Jaccard's Coefficient(JC) to formalize the hierarchy structure after pre-processing data. We implemented our method using two Linked Data Sets: DrugBank and Diseasome from Linked Life Data and evaluated performance with the gold standard.