Neo4j graph database realizes efficient storage performance of oilfield ontology

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.

[1]  Örjan Ekeberg,et al.  Large-Scale Modeling – a Tool for Conquering the Complexity of the Brain , 2008, Frontiers Neuroinformatics.

[2]  Lina Nemuraite,et al.  TRANSFORMING ONTOLOGY REPRESENTATION FROM OWL TO RELATIONAL DATABASE , 2006 .

[3]  Tanya Z. Berardini,et al.  TermGenie – a web-application for pattern-based ontology class generation , 2014, J. Biomed. Semant..

[4]  Alfonso Rodríguez-Patón,et al.  A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights , 2018, Neural Processing Letters.

[5]  M. Punithavalli,et al.  Performance Evaluation of Semantic Based and Ontology Based Text Document Clustering Techniques , 2012 .

[6]  R. Vijayakumar,et al.  Ontology based data integration of NoSQL datastores , 2014, 2014 9th International Conference on Industrial and Information Systems (ICIIS).

[7]  Abdel-Badeeh M. Salem,et al.  Large-scale ontology storage and query using graph database-oriented approach: The case of Freebase , 2015, 2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS).

[8]  Ming-jian Zhou,et al.  A framework for ontology-based knowledge management , 2011, 2011 International Conference on Business Management and Electronic Information.

[9]  Daofeng Sun,et al.  A multi-aromatic hydrocarbon unit induced hydrophobic metal–organic framework for efficient C2/C1 hydrocarbon and oil/water separation , 2017 .

[10]  René Peinl,et al.  Performance of graph query languages: comparison of cypher, gremlin and native access in Neo4j , 2013, EDBT '13.

[11]  Lei Zou,et al.  Semantic SPARQL Similarity Search Over RDF Knowledge Graphs , 2016, Proc. VLDB Endow..

[12]  MengChu Zhou,et al.  An Incremental and Distributed Inference Method for Large-Scale Ontologies Based on MapReduce Paradigm , 2015, IEEE Transactions on Cybernetics.

[13]  Peng Peng,et al.  Processing SPARQL queries over distributed RDF graphs , 2014, The VLDB Journal.

[14]  Wu YaNing,et al.  A Method of Semantic Annotation and Ontology Construction for Unified Command and Control Language , 2013, 2013 10th Web Information System and Application Conference.

[15]  Bin Wang,et al.  Constructing DNA Barcode Sets Based on Particle Swarm Optimization , 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[16]  Liang Feng,et al.  Pore-Environment Engineering with Multiple Metal Sites in Rare-Earth Porphyrinic Metal-Organic Frameworks. , 2018, Angewandte Chemie.

[17]  Romain Brette,et al.  Neuroinformatics Original Research Article Brian: a Simulator for Spiking Neural Networks in Python , 2022 .

[18]  Carsten Binnig,et al.  RODI: A Benchmark for Automatic Mapping Generation in Relational-to-Ontology Data Integration , 2015, ESWC.

[19]  Jing Wang,et al.  The Applied Research of the Method in Ontology Mapping Based on the Relational Model , 2013 .

[20]  Walter Terkaj,et al.  Ontology-based modeling of production systems for design and performance evaluation , 2014, 2014 12th IEEE International Conference on Industrial Informatics (INDIN).

[21]  Xun Wang,et al.  Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control , 2016, Inf. Sci..

[22]  Hassan Naderi,et al.  ExPregel: a new computational model for large‐scale graph processing , 2015, Concurr. Comput. Pract. Exp..

[23]  Haruo Yokota,et al.  JARS: Join-Aware Distributed RDF Storage , 2016, IDEAS.

[24]  Xun Wang,et al.  Small Universal Bacteria and Plasmid Computing Systems , 2018, Molecules.

[25]  Olaf Hartig,et al.  Reconciliation of RDF* and Property Graphs , 2014, ArXiv.

[26]  A. Sheth International Journal on Semantic Web & Information Systems , .

[27]  O. P. Vyas,et al.  An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storage , 2015, Knowl. Based Syst..

[28]  Daniel P. Miranker,et al.  On directly mapping relational databases to RDF and OWL , 2012, WWW.

[29]  Romain Brette,et al.  Brian: A Simulator for Spiking Neural Networks in Python , 2008, Frontiers Neuroinformatics.

[30]  Xiangrong Liu,et al.  Asynchronous spiking neural P systems with rules on synapses , 2015, Neurocomputing.

[31]  Seiji Isotani,et al.  Ontology Driven Software Engineering: A Review of Challenges and Opportunities , 2015, IEEE Latin America Transactions.

[32]  Xiangxiang Zeng,et al.  Spiking Neural P Systems With Colored Spikes , 2018, IEEE Transactions on Cognitive and Developmental Systems.

[33]  Hojjat Adeli,et al.  Spiking Neural Networks , 2009, Int. J. Neural Syst..

[34]  R. Spitz,et al.  Catalyseurs Zieger-Natta supportés sur MgCl2 pour la polymérisation stéréorégulée du propène. II. Système catalytique simple: Solide binaire MgCl2-TiCl4 et solution cocatalytique triéthylaluminium, benzoate d'ethyle , 1984 .

[35]  Pan Zheng,et al.  On the Computational Power of Spiking Neural P Systems with Self-Organization , 2016, Scientific Reports.

[36]  Quang Hoang,et al.  An Approach of Transforming Ontologies into Relational Databases , 2015, ACIIDS.

[37]  Bin Wang,et al.  Correcting Errors in Image Encryption Based on DNA Coding , 2018, Molecules.

[38]  RaniMonika,et al.  An ontology-based adaptive personalized e-learning system, assisted by software agents on cloud storage , 2015 .