Alignment-Based Preprocessing of Personal Ontologies on Semantic Social Network

Authors:
Jason J. Jung;Hong-Gee Kim;Geun-Sik Jo
Affiliations:
Inha University, Korea;Seoul National University, Korea;Inha University, Korea
Venue:
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Year:
2007

Citing 7
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Abstract

In semantic social network, the relations between users are inferred by measuring the similarity between the corresponding personal ontologies. However, "over-enriched" personal ontologies have caused some difficulties in being discriminated from other personal ontologies. For efficiently annotating resources in their own repositories, people simply append ontology fragments retrieved from standard ontologies and from other neighbors' personal ontologies along to social links. In this paper, we propose a preprocessing method to extract preferential concepts for comparing with social semantics. In order to prune out irrelevant concepts from personal ontologies, alignment-based concept classification process is designed by checking these two main criteria; i) redundancy (e.g., if there already exist semantically identical concepts), and ii) tendency (e.g., if there exist semantically declined concepts). Finally, we want to show an application scenario to demonstrate our contributions.