Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Instance Selection and Construction for Data Mining
Instance Selection and Construction for Data Mining
A Unifying View on Instance Selection
Data Mining and Knowledge Discovery
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Systems for Knowledge Discovery in Databases
IEEE Transactions on Knowledge and Data Engineering
Structured databases on the web: observations and implications
ACM SIGMOD Record
Semantic Web Technologies: Trends and Research in Ontology-based Systems
Semantic Web Technologies: Trends and Research in Ontology-based Systems
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Data Mining: A Knowledge Discovery Approach
Data Mining: A Knowledge Discovery Approach
Extracting generalization hierarchies from relational databases: A reverse engineering approach
Data & Knowledge Engineering
Building ontologies from relational databases using reverse engineering methods
CompSysTech '07 Proceedings of the 2007 international conference on Computer systems and technologies
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
A flexible framework to experiment with ontology learning techniques
Knowledge-Based Systems
FARS: A Multi-relational Feature and Relation Selection Approach for Efficient Classification
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Application-oriented purely semantic precision and recall for ontology mapping evaluation
Knowledge-Based Systems
Design issues for knowledge artifacts
Knowledge-Based Systems
Research and Implementation of Ontology Automatic Construction Based on Relational Database
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 05
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
Design and Implementation of Mapping Rules from OWL to Relational Database
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04
Mapping ER Schemas to OWL Ontologies
ICSC '09 Proceedings of the 2009 IEEE International Conference on Semantic Computing
Translating relational & object-relational database models into OWL models
IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Semantic Web computing in industry
Computers in Industry
On how to perform a gold standard based evaluation of ontology learning
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Dynamic generation of concepts hierarchies for knowledge discovering in bio-medical linked data sets
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
BioOntoVerb: A top level ontology based framework to populate biomedical ontologies from texts
Knowledge-Based Systems
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Relational Database (RDB) has been widely used as the back-end database of information system. Contains a wealth of high-quality information, RDB provides conceptual model and metadata needed in the ontology construction. However, most of the existing ontology building approaches convert RDB schema without considering the knowledge resided in the database. This paper proposed the approach for ontology extraction on top of RDB by incorporating concept hierarchy as background knowledge. Incorporating the background knowledge in the building process of Web Ontology Language (OWL) ontology gives two main advantages: (1) accelerate the building process, thereby minimizing the conversion cost; (2) background knowledge guides the extraction of knowledge resided in database. The experimental simulation using a gold standard shows that the Taxonomic F-measure (TF) evaluation reaches 90% while Relation Overlap (RO) is 83.33%. In term of processing time, this approach is more efficient than the current approaches. In addition, our approach can be applied in any of the fields such as eGoverment, eCommerce and so on.