Abstract
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.
Highlights
Data Availability Statement: The data underlying the results presented in the study are available from Berlin SPARQL Benchmark (BSBM) repository
We propose a domain ontology building process based on the Neo4j graphics database and a retrieval method based on a two-tier index architecture
These methods are the main steps toward building large-scale domain ontology
Summary
The appropriate ontology storage method is conducive to the extension of the ontology and the improvement of query efficiency. Since relational database data are stored in a two-dimensional table, and the ontology model uses the map structure of directed graph, an impedancemismatch problem will occur in the conversion process [9]. Vysniauskas [12] focuses on the characteristics of OWL ontology and attributes They improve the existing schema by setting the relational table and adding the relation constraint table Trend to make it easier to implement the information storage of classes, attributes and complex relationships in the OWL ontology. Its primitive consists of three elements: node, relationship and attribute, which can completely describe the situation of many users The advantage of this storage model is that the node attributes of the storage model can be added or deleted at any time, effectively solving the problem of semi-structured, unstructured data storage and memory waste. The OWL is used to build RDF datasets in the
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