An Effective Semantic Web Knowledge Processing Mechanism by Using an Adaptive Swarm Intelligence Technique for Ontology (ASITO)

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2021 by IJETT Journal
Volume-69 Issue-3
Year of Publication : 2021
Authors : A.Sindhura, J.Rajeshwar, M.V.Narayana, M.Ram Babu
DOI :  10.14445/22315381/IJETT-V69I3P230


MLA Style: A.Sindhura, J.Rajeshwar, M.V.Narayana, M.Ram Babu"An Effective Semantic Web Knowledge Processing Mechanism by Using an Adaptive Swarm Intelligence Technique for Ontology (ASITO)" International Journal of Engineering Trends and Technology 69.3(2021):195-200. 

APA Style:A.Sindhura, J.Rajeshwar, M.V.Narayana, M.Ram Babu. An Effective Semantic Web Knowledge Processing Mechanism by Using an Adaptive Swarm Intelligence Technique for Ontology (ASITO)  International Journal of Engineering Trends and Technology, 69(3),195-200.

The Semantic Web offers a comprehensive solution to individuals gaining supremacy of various data and data resources. The semantic web provides people with the tools to render data more concrete and less biased. The Semantic Web often extends to certain facets of our everyday lives. There is new scope for the usage and discovery of knowledge because of the semantic web. A field of focus is developing knowledge processing. The semantic web is a leading representative of Semantic Web development and discoveries. The semantic Network allows researchers to reflect their data in languages that are simpler to be processed, incorporated, and reason. Data creates much greater importance if connected to other valid data services. The data convergence from mash-ups to the business is a fascinating subject in current research and growth. As intelligent goods are being created, thinking abilities will bring much more benefit. It offers many issues to worry about to bring one across. Adaptive Swarm Intelligence Technique for Ontology (ASITO) addresses the increasing knowledge base, and reasoning efficiency needs to be modernized.

[1] C. Bizer, A. Primpeli, and R. Peeters., Using the Semantic Web as a source of training data, Datenbank-Spektrum, 19(2)(2019) 127–135. doi: 10.1007/s13222-019-00313-y.
[2] Ranpara R., Yusufzai A., Kumbharana C.K., A Comparative Study of Ontology Building Tools for Contextual Information Retrieval. In: Rathore V., Worring M., Mishra D., Joshi A., Maheshwari S. (eds) Emerging Trends in Expert Applications and Security. Advances in Intelligent Systems and Computing, Springer, Singapore, 841(2019).
[3] Wang, R., C. Ives, AND S. Edwards., Ontology-based Semantic Mapping of Chemical Toxicities. TOXICOLOGY, Elsevier Science Ltd, New York, NY, 412(2019) 89-100.
[4] Zhang, Y., Li, C., Chen, N., Liu, S., Du, L., Wang, Z., & Ma, M., Semantic web and unique geospatial features based geospatial data integration. In Geospatial Intelligence: Concepts, Methodologies, Tools, and Applications, IGI Global,(2019) 230-253.
[5] Lars Vogt, Roman Baum, Christian Köhler, Sandra Meid, Björn Quast, Peter Grobe., Using Semantic Programming for Developing a Web Content Management System for Semantic Phenotype Data, In International Conference on Data Integration in the Life Sciences, 200-206.
[6] Zhang, Q., DiFranzo, D., Gloria, M. J. K., Makni, B., & Hendler, J. A., Analyzing the Flow of Trust in the Virtual World with Semantic Web Technologies, IEEE Transactions on Computational Social Systems, (99)(2018) 1-9.
[7] Darko Andro?ec, Matija Novak and Dijana Oreški, Using Semantic Web for the Internet of Things Interoperability: A Systematic Review, International Journal on Semantic Web and Information Systems (IJSWIS), 14(4)(2018) 147-171.
[8] Allemang, D. and Hendler, J., Semantic web for the working ontologist: 2nd Edition effective modeling in RDFS and OWL. Elsevier, (2011).
[9] Antoniou, G. et al., A survey of large-scale reasoning on the web of data, The Knowledge Engineering Review. Cambridge University Press, 33(2018).
[10] Bergman, M. K., Platforms and Knowledge Management, in A Knowledge Representation Practionary. Springer,(2018) 251-272.
[11] Moussallem, D., Wauer, M., & Ngomo, A. C. N., Machine translation using semantic web technologies: A survey. Journal of Web Semantics, 51(2018) 1-19.
[12] Scioscia, F., Ruta, M., Loseto, G., Gramegna, F., Ieva, S., Pinto, A., and Di Sciascio, E., Mini-ME matchmaker and Reasoner for the Semantic Web of Things. Innovations, Developments, and Applications of Semantic Web and Information Systems, (2018) 262-294.
[13] Gayo, J.E.L., Prud`Hommeaux, E., Boneva, I. and Kontokostas, D., Validating RDF Data. Synthesis Lectures on Semantic Web: Theory and Technology, 7(1)(2017) 1-328.
[14] Khamparia, A. and Pandey, B., Comprehensive analysis of semantic web reasoners and tools: a survey. Education and Information Technologies, 22(6)(2017) 3121-3145.
[15] Sarker, M. K., Xie, N., Doran, D., Raymer, M., & Hitzler, P., Explaining trained neural networks with semantic web technologies: First steps. arXiv preprint arXiv:1710.04324, (2017).
[16] G.Balakrishna and Moparthi Nageshwara Rao., Study Report on Using IoT Agriculture Farm Monitoring, Innovati1ons in Computer Science and Engineering, Lecture Notes in Networks and Systems, 74.
[17] G.Balakrishna and Moparthi Nageshwara Rao., ESBL: Design and Implement A Cloud Integrated Framework for IoT Load Balancing, International Journal Of Computers Communications & Control ISSN 1841-9836, e-ISSN 1841-9844, 14(4)(2019) 459-474.
[18] G.Balakrishna and Moparthi Nageshwara Rao., Study report on Indian agriculture with IoT International Journal of Electrical and Computer Engineering.
[19] G. Balakrishna and Nageswara Rao Moparthi., The Automatic Agricultural Crop Maintenance System using Runway Scheduling Algorithm: Fuzzyc-LR for IoT Networks, International Journal of Advanced Computer Science and Applications,(IJACSA), 11(11)(2020).

Semantic Web; Ontology; Swarm Intelligence; Particle Swarm Optimization. Introduction