The Research on Cloud Manufacturing Service Discovery Strategy Based on Mapping Reuse

Abstract:

In view of the heterogeneous gulf between service providers and resource service demanders in cloud manufacturing platform, an ontology mapping reusing algorithm was proposed to improve the performance of service discovery. Firstly, a semantic bipartite graph between the concepts profile was built to get the similarity of neighboring concepts from the best match of the bipartite graph. Then inspired by the similarity transmission between neighboring concepts, whether the new mapping should be established was decided by the existing mapping and the similar degree between the neighboring concepts. Finally, I testified the algorithm by an experiment.

• Periodical: Advanced Materials Research (Volumes 860-863)
• Pages: 2898-2901
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.860-863.2898
• Online since: December 2013
• Authors: Zhen Zhao*
• Keywords: Cloud Manufacturing, Mapping Reuse, Service Discovery

References

[1] Noy N F, Musen M A. Anchor-PROMPT: Using Non-Local Context for Semantic Matching[C]. Workshop on Ontologies and Information Sharing at the International Joint Conference on Artificial Intelligence, 2001. 63-70.

[2] Madhavan J, Bernstein P A, Doan A, et al. Corpus-based schema matching[C]. 21st International Conference on Data Engineering, 2005. 57-68.

DOI: 10.1109/icde.2005.39

[3] Aumueller D, Do H, Massmann S, et al. Schema and ontology matching with COMA++[C]. ACM SIGMOD international conference on Management of data, New York, ACM, 2005, 906-908.

DOI: 10.1145/1066157.1066283

[4] Aleksovski Z, Kate W T, Van Harmelen F. Ontology matching using comprehensive ontology as background knowledge[C]. International Workshop on Ontology Matching at ISWC 2006, CEUR, (2006).

DOI: 10.1007/11891451_18

[5] Liu X J, Wang Y L, Wang J. Towards a Semi-Automatic Ontology Mapping-An Approach Using Instance Based Learning and Logic Relation Mining[C]. Fifth Mexican International Conference on Artificial Intelligence, 2006. 269-280.

DOI: 10.1109/micai.2006.46

[6] Wang Y, Liu X, Chen J. A multi-stage strategy for ontology mapping resolution[C]. IEEE International Conference on Information Reuse and Integration, 2008. 345-350.

DOI: 10.1109/iri.2008.4583055

[7] Wang S, Englebienne G, Schlobach S. Learning Concept Mappings from Instance Similarity[C]. 7th International Semantic Web Conference, Karlsruhe, Germany, 2008. 339-355.

DOI: 10.1007/978-3-540-88564-1_22

[8] Melnik S, Garcia-Molina H, Rahm E. Similarity flooding: a versatile graph matching algorithm and itsapplication to schema matching[C]. 18th International Conference on Data Engineering, 2002, 117-128.

DOI: 10.1109/icde.2002.994702

[9] Gusfield D. Algorithms on Stings, Trees, and Sequences: Computer Science and Computational Biology[J]. ACM SIGACT News. 1997, 28(4): 41-60.

DOI: 10.1145/270563.571472

[10] Ehrig M, Sure Y, Staab S. Supervised Learning of an Ontology Alignment Process[J]. Professional Knowledge Management. 2005: 508-517.

DOI: 10.1007/11590019_58