Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Nov 16;13(11):e0207595.
doi: 10.1371/journal.pone.0207595. eCollection 2018.

Neo4j Graph Database Realizes Efficient Storage Performance of Oilfield Ontology

Free PMC article

Neo4j Graph Database Realizes Efficient Storage Performance of Oilfield Ontology

Faming Gong et al. PLoS One. .
Free PMC article


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.

Conflict of interest statement

The affiliaition with China Petroleum and Chemical Corporation Shengli Oilfield Branch Ocean Oil Production Plant does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.


Fig 1
Fig 1. Ontology construction flow chart.
Fig 2
Fig 2. An RDF graph and corresponding triples relationship.
Fig 3
Fig 3. The data structure of Neo4j.
Fig 4
Fig 4. An example of an RDF directional marker map in the oilfield ontology.
Fig 5
Fig 5. Neo4j database stored procedures.
Fig 6
Fig 6. Mapping of RDF directed graph to Neo4j data structure.
Fig 7
Fig 7. Graphic structure of multiple sets of triples in relational degree retrieval.
Fig 8
Fig 8. Storage capacity comparison chart.
Fig 9
Fig 9. Query time-consumption comparison chart.

Similar articles

See all similar articles

Cited by 1 article


    1. Isotani S, Bittencourt I I, Barbosa E F, Dermevalet D, Paiva R O A. Ontology Driven Software Engineering: A Review of Challenges and Opportunities. IEEE Latin America Transactions. 2015; 13(3): 863–869. 10.1109/TLA.2015.7069116 PMID: 15019795 - DOI
    1. Kiran V K, Vijayakumar R. Ontology based data integration of NoSQL datastores. International Conference on Industrial and Information Systems. 2015; 1–6 10.1109/ICIINFS.2014.7036545 PMID: 14920284 - DOI
    1. Sequeda J F, Arenas M, Miranker D P. On directly mapping relational databases to RDF and OWL. International Conference on World Wide Web. 2012; 649–658 10.1145/2187836.2187924 - DOI
    1. Liu B, Huang K, Li J, Zhou M. An incremental and distributed inference method for large-scale ontologies based on mapreduce paradigm. IEEE Transactions on Cybernetics. 2014; 45(1): 53–64. 10.1109/TCYB.2014.2318898 - DOI - PubMed
    1. Punitha S C, Punithavalli M. Performance evaluation of semantic based and ontology based text document clustering techniques. Procedia Engineering. 2012; 30:100–106. 10.1016/j.proeng.2012.01.839 - DOI

Publication types

Grant support

This work was supported by the Chinese Ministry of Science and Technology Innovation Work (Grant No. 2015IM010300 to FG). China Petroleum and Chemical Corporation Shengli Oilfield Branch Ocean Oil Production Plant provided support in the form of salaries for authors, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.