Abstract
Graph models do excel where data have an element of uncertainty or unpredictability and the relationships are data’s main features. However, existing graph models neglect the semantics of node and relationship type.
To capture as much semantics as possible, we extend the nodes in graph model with some object-oriented features and edges with multiple semantic information, and propose a Semantic Graph Model (SGM). SGM is a schema-less model and supports dynamic data structures as well as extra semantics. Although the class definition is unknown at the beginning, the schema can be extracted from the semi-structured and semantic data. The excavated domain model can help further data analysis and data fusion, and it is also important for graph query optimization.
We have proposed graph create statements to represent data in SGM and have implemented a conversion layer to store, manage and query the graph upon the graph database system, Neo4j.
This work is supported by National Natural Science Funds of China under grand numbers 61202100 and 61272110.
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Chen, L., Yu, T., Liu, M. (2015). A Semantic Graph Model. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2015 Conferences. OTM 2015. Lecture Notes in Computer Science(), vol 9415. Springer, Cham. https://doi.org/10.1007/978-3-319-26148-5_25
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DOI: https://doi.org/10.1007/978-3-319-26148-5_25
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