Tag-Based Integrated Semantic Ontology Construction and Evolution

In this research, we propose a methodology for ontology construction and evolution using tags from tag-cloud to improve availability of analysis and utilization of large scale of data. It is possible to construct semantic relationships among tags. To do this, one way of building of relationships among tags is use of a popularized tag as a thing of ontology. However, it is not easy to select the popularized tag from tag cloud and to build a complete ontology with all of tags in the tag-cloud because tags are timely and dynamically updated added with tremendous numbers of tags. Therefore, we construct Primitive Ontologies (POs). A general tag is used to as a thing of a PO. The general tag is matched into one of requested keywords by users. In addition, we propose a methodology to evolve ontology using tags. To construct and evolve a PO, it defines classes and relationships between given tags by a user. Finally, the constructed several POs are integrated into a Tag-Based integrated Semantic ontology (TBiSont) by building relationships between classes that are included in each different PO. The construction of relationships among tags depends on linked resources into tags. The properties of tags are usually characterized by linked resources. It causes to extract semantic relationships among tags, because tags are comprised of meta-information that represents properties of resources. Thereby, the proposed PO is constructed by a general tag as a best-referring tag and relationships among tags including the general tag. The TBiSont as an integration of POs was applied into data analysis and a search process to verify efficiency of as in application systems. When we applied TBiSont into searching, it shows that TBiSont is more efficient than the other statistical-based search systems to increase accuracy or diversity of search results.