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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References
Bondy, J.A., Murty, U.S.R.: Graph theory with applications. Macmillan, London (1976)
Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Int. J. Semantic Web Inf. Syst., 1–22 (2009)
Madhavan, J., Halevy, A.Y., Cohen, S., Dong, X.L., Jeffery, S.R., Ko, D., Yu, C.: Structured data meets the web: A few observations. IEEE Data Eng. Bull. 29(4), 19–26 (2006)
Talukdar, P.P., Ives, Z.G., Pereira, F.: Automatically incorporating new sources in keyword search-based data integration. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 387–398. ACM (2010)
Klyne, G., Carroll, J.J., McBride, B.: RDF 1.1 Concepts and Abstract Syntax (2014). http://www.w3.org/TR/rdf11-concepts/
Isaac, A., Summers, E.: SKOS Simple Knowledge Organization System Primer (2009). http://www.w3.org/TR/2009/NOTE-skos-primer-20090818/
Hitzler, P., Krotzsch, M., Parsia, B., Patel-Schneider, P.F., Rudolph, S.: OWL 2 Web Ontology Language Primer, 2nd edn. (2012). http://www.w3.org/TR/2012/REC-owl2-primer-20121211/
Suchanek, F., Weikum, G.: Knowledge harvesting in the big-data era. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 933–938. ACM (2013)
Bizer, C., Boncz, P., Brodie, M.L., Erling, O.: The meaningful use of big data: four perspectives-four challenges. ACM SIGMOD Record 40(4), 56–60 (2012)
Dong, X.L., Srivastava, D.: Big data integration. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE), pp. 1245–1248. IEEE (2013)
Hull, R., King, R.: Semantic database modeling: Survey, applications, and research issues. ACM Computing Surveys (CSUR) 19(3), 201–260 (1987)
Flesca, S., Greco, S.: Querying graph databases. In: Zaniolo, C., Grust, T., Scholl, M.H., Lockemann, P.C. (eds.) EDBT 2000. LNCS, vol. 1777, pp. 510–524. Springer, Heidelberg (2000)
Rodriguez, M.A., Neubauer, P.: The graph traversal pattern. In: Graph Data Management, pp. 29–46 (2011)
Wood, P.T.: Query languages for graph databases. SIGMOD Record, 50–60 (2012)
Miller, J.J.: Graph database applications and concepts with neo4j. In: Proceedings of the Southern Association for Information Systems Conference, Atlanta, GA, USA, March 23–24, 2013
Robinson, I., Webber, J., Eifrem, E.: Graph Databases, 2nd edn. O’Reilly Media Inc., USA (2015)
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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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