Relational Database Ontology Discovery Method Based on Formal Concept Analysis
Zhi-Yong Gao, Yong-Quan Liang, Shu-Han Qiao
Available Online December 2016.
- https://doi.org/10.2991/mme-16.2017.101How to use a DOI?
- Database, Ontology, Formal concept analysis.
- This paper elaborates how to use the formal concept analysis method to map contents in the database to the ontology, in order to provide the big data application with high-quality data source by virtue of integrating the database with Semantic Web. In recent times, a mass of data is stored in the relational database, but such data with low share usage fails to play its full role. On account that the big data application has grown by leaps and bounds, a large number of shared data is urgently needed. By mapping the data in the relational database into the ontology, the technology of Semantic Web can provide a lot of semantic data to the big data application, which is conducive to big data analysis and use. In this paper, the ontology is built by taking the formal concept as the intermediate model and converting the logic structure of database into Hasse graph and context table, and then combining with the domain knowledge. The ontology in the knowledge domain can be found from the database by applying the formal concept analysis method, which takes full advantage of logical structure information of the database and is beneficial for automation found by the ontology. Eventually, ontology method and problems found in the relational database by virtue of the formal concept analysis are summarized herein..
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Zhi-Yong Gao AU - Yong-Quan Liang AU - Shu-Han Qiao PY - 2016/12 DA - 2016/12 TI - Relational Database Ontology Discovery Method Based on Formal Concept Analysis BT - 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016) PB - Atlantis Press SP - 727 EP - 735 SN - 2352-5401 UR - https://doi.org/10.2991/mme-16.2017.101 DO - https://doi.org/10.2991/mme-16.2017.101 ID - Gao2016/12 ER -