Secure and Efficient Graph Derivation Representation Approach for Measuring and Distributing Cluster Based Ontology
DOI:
https://doi.org/10.53555/nnms.v2i3.556Keywords:
Ontology, Ontology reuse, Ontology measure, SET-IBS, SET-IBOOS, GraphAbstract
Secure data transmission for cluster-based measuring and comparing ontologies, where the clusters are formed dynamically and occasionally. We intend two sheltered and resourceful records Transmission(SET) protocols for CWSNs, is SET-IBS and SET-IBOOS, through by means of the IdentityBased digital Signature (IBS) scheme and the Identity-Based Online/Offline digital Signature (IBOOS) scheme in that order. During SET-IBS, protection relies on the rigidity of the DiffieHellman problem in the pairing domain. SET-IBOOS further reduces the computaional overhead for present a graph derivation representation based approach (GDR) for stable semantic quantity, which captures structural semantics of ontologies, which is while its serving relies on the hardness of the discrete logarithm problem. The calculations and simlations are provided to illustrate the efficiency of the new protocols. The domino effect illustrate to, the future protocols have better performance than the existing secure protocols for measuring and comparing ontologies, in terms of security overhead and energy consumption.
References
Yinglong Ma, Ling Liu, Senior Member, IEEE,Ke Lu, Beihong Jin, and Xiangjie Liu “ A Graph Derivation based approach for and comparing structural semantics of ontologies” VOL. 26, NO. 5, MAY 2014
M. d’Aquin and N. F. Noy, “Where to publish and find ontologies? A survey of ontology libraries,” J. Web Semant., vol. 11, no. 8,pp. 96–111, 2012.
Z. Khan and M. Keet, “ONSET: Automated foundational ontology selection and explanation,” in Proc. 18th Int. Conf. EKAW, Galway City, Ireland, 2012, pp. 237–251.
A. M. Khattak, Z. Pervez, K. Latif, and S. Lee, “Time efficient reconciliation of mappings in dynamic web ontologies,” Knowl. Based Syst., vol35,. no. 11, pp. 369–374, 2012.
D. Sanchez, M. Batet, D. Isern, and A. Valls, “Ontology-based semantic similarity: A new feature-based approach,” Expert Syst.
Applicat., vol. 39, no. 9, pp. 7718–7728, 2012.
F. Ensan and W. Du. “A semantic metrics suite for evaluating modular ontologies,” Inform. Syst., vol. 38, no. 5, pp. 745–770,
L. Razmerita, “An ontology-based framework for modeling userbehavior-A case study in knowledge management,” IEEE
Trans.Syst., Man, Cybern. A, Syst., Humans, vol. 41, no. 4, pp. 772–783, Jul. 2011.
J. Park, S. Oh, and J. Ahn, “Ontology selection ranking model for knowledge reuse,” Expert Syst. Applicat., vol. 38, no. 10, pp.
–5144, 2011.
H. Zhang, Y.-F. Li, and H. B. K. Tan, “Measuring design complexity of semantic web ontologies,” J. Syst. Softw., vol. 83, no. 5, pp. 803–814, 2010.
R. Kontchakov, F. Wolter, and M. Zakharyaschev, “Logicbased ontology comparison and module extraction, with an application to DL-Lite,” Artif. Intell., vol. 174, no. 15, pp. 1093–1141, 2010.
Y. Ma, B. Jin, and Y. Feng. “Semantic oriented ontology cohesion metrics for ontology-based systems,” J. Syst. Softw., vol.
, no. 1,pp. 143–152, 2010.
Y. Ma, “Towards stable semantic ontology measurement,” in Proc. ISWC, 2010, pp. 21–24
Z. Zou, J. Li, H. Gao, and S. Zhang, “Mining frequent subgraph patterns from uncertain graph data,” IEEE Trans. Knowl. Data Eng.,vol. 22, no. 9, pp. 1203–1218, Sept. 2010.
M. Mao, Y. Peng, and M. Spring, “An adaptive ontology mappingapproach with neural network based constraint satisfaction,”J. Web Semantics, vol. 8, no. 1, pp. 14–25, 2010.
J. Li, J. Tang, Y. Li, and Q. Luo, “RiMOM: A dynamic multistrategy ontology alignment framework,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 8, pp. 1218–1232, Aug. 2009.
H. Zhuge, “Communities and emerging semantics in semanticlink network: Discovery and learning,” IEEE Trans. Knowl. Data Eng., vol. 21, no. 6, pp. 785–799, Jun. 2009.
B. Motik, B. C. Grau, I. Horrocks, and U. Sattler, “Representing ontologies using description logics, description graphs, and rules,” Artif. Intell., vol. 173, no. 14, pp. 1275–1309, 2009.
S. Rudolph, M. Krotzsch, and P. Hitzler, “Description logic reasoning with decision diagrams: Compiling SHIQ to disjunctive datalog,” in Proc. ISWC, 2008, pp. 435–450
A. Burton-Jones, V. C. Storey, V. Sugumaran, and P. Ahluwalia,“A semiotic metrics suite for assessing the quality of
ontologies,”Data Knowl. Eng., vol. 55, no. 1, pp. 84–102, 2009.
H. Stuckenschmidt, “A semantic similarity measure for ontologybased information,” in Proc. 8th Int. Conf. FQAS, Roskilde,Denmark, 2009, pp. 406–417.
M. Popescu, J. M. Keller, and J. A. Mitchell, “Fuzzy measures on the gene ontology for gene product similarity,” IEEE/ACM Trans. Comput. Bio. and Bioinfo., vol. 3, no. 3, pp. 263–274, Jul./Sept. 2009.
H. Al-Mubaid and H. Nguyen, “Measuring semantic similarity between biomedical concepts within multiple ontologies,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 39, no. 4, pp. 389–398Jul. 2009.
D. Fensel, “Ontology-based knowledge management,” IEEE Comput., vol. 35, no. 11, pp. 56–59, Nov. 2008.
L. Chen, N. R. Shadbolt, and C. A. Goble, “A semantic web-based approach to knowledge management for grid applications,”
IEEE Trans. Knowl. Data Eng., vol. 19, no. 2, pp. 283–296, Feb. 2008.
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