Text2Onto: a framework for ontology learning and data-driven change discovery
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
On the conceptual tag refinement
Proceedings of the 2008 ACM symposium on Applied computing
A Hybrid Approach for Learning Concept Hierarchy from Malay Text Using GAHC and Immune Network
ICARIS '09 Proceedings of the 8th International Conference on Artificial Immune Systems
SALT: A semantic adaptive framework for monitoring citizen satisfaction from e-government services
Expert Systems with Applications: An International Journal
An user interface adaptation architecture for rich internet applications
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
An adaptive e-questionnaire for measuring user perceived portal quality
International Journal of Human-Computer Studies
An ontological representation of public services: models, technologies and use cases
Journal of Web Engineering
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In this paper we present a novel method for ontology development that combines ontology learning and social-tagging process. The approach is based on the idea of using tagging process as a method for refinement (pruning) of the ontology that has been learned automatically from available knowledge sources. In the nutshell of the approach is a model for the conceptual tag refinement, which basically searches for terms that are conceptually related to the tags that are assigned to an information source. In that way the meaning of the tags can be disambiguated, which support better usage of the tagging process for the ontology pruning. We have developed a software tool, an annotation framework, which realizes this idea. We present results from the first evaluation studies regarding the application of this approach in the eGovernment domain.