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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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GOMS: Large-scale ontology management system using graph databases

  • Lee, Chun-Hee (Intelligence Information Research Division, Electronics and Telecommunications Research Institute) ;
  • Kang, Dong-oh (Intelligence Information Research Division, Electronics and Telecommunications Research Institute)
  • Received : 2021.07.23
  • Accepted : 2021.09.27
  • Published : 2022.10.10
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Abstract

Large-scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or object-oriented DBMSs (OODBMSs) to develop large-scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large-scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database-based ontology management system (GOMS) to efficiently manage large-scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash-based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.

Keywords

Acknowledgement

This work was supported by Electronics and Telecommunications Research Institute(ETRI) grant funded by the Korean government. [21ZS1100, Core Technology Research for Self-Improving Integrated Artificial Intelligence System]

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