Department of Computer Technology, College of Computer and Communication Engineering, China University of Petroleum, Qingdao, Shandong, China.
China Petroleum and Chemical Corporation Shengli Oilfield Branch Ocean Oil Production Plant, Dongying, Shandong, China.
PLoS One. 2018 Nov 16;13(11):e0207595. doi: 10.1371/journal.pone.0207595. eCollection 2018.
The integration of oilfield multidisciplinary ontology is increasingly important for the growth of the Semantic Web. However, current methods encounter performance bottlenecks either in storing data and searching for information when processing large amounts of data. To overcome these challenges, we propose a domain-ontology process based on the Neo4j graph database. In this paper, we focus on data storage and information retrieval of oilfield ontology. We have designed mapping rules from ontology files to regulate the Neo4j database, which can greatly reduce the required storage space. A two-tier index architecture, including object and triad indexing, is used to keep loading times low and match with different patterns for accurate retrieval. Therefore, we propose a retrieval method based on this architecture. Based on our evaluation, the retrieval method can save 13.04% of the storage space and improve retrieval efficiency by more than 30 times compared with the methods of relational databases.
油田多学科本体的集成对于语义网的发展越来越重要。然而,当前的方法在处理大量数据时,无论是在存储数据还是在搜索信息方面,都存在性能瓶颈。为了克服这些挑战,我们提出了一种基于 Neo4j 图形数据库的领域本体处理方法。在本文中,我们专注于油田本体的数据存储和信息检索。我们设计了从本体文件到规范 Neo4j 数据库的映射规则,这可以大大减少所需的存储空间。采用了两层索引架构,包括对象索引和三元组索引,以保持加载时间低,并与不同模式匹配,实现准确检索。因此,我们提出了一种基于该架构的检索方法。通过评估,与关系型数据库的方法相比,该检索方法可以节省 13.04%的存储空间,并将检索效率提高 30 多倍。