Data of Semantic Web as Unit of Knowledge
Keywords:
Semantic Web, Ontology, Unit of Knowledge, Knowledge Representation schemeAbstract
In service to the state of the art, advances are required toward redesigning the framework over which web applications are built. The semantic web lies at the intersection of web and machine understandable meaningful data, turning it into intelligent ‘web of data’. The key requirement with any intelligent system has been to find a concrete knowledge representation that can make the inferences within time and space constraints; that is, reasoning effectively and efficiently within the resource constraints posed to the problem at one hand and with insufficient data as well as incomplete knowledge on the other hand. Various Knowledge representation schemes have been proposed in the literature, each having its limitation over the others. Ontology is the key component for semantic web engineering. Ontologies are conceptual knowledge bases providing a systematic and taxonomical description of the concepts and instances under consideration. Conceptual clarity in the computational representation of a concept is vital for holistic thinking and knowledge engineering. In order to meet the needs of an application/enterprise, knowledge should be presented taking care of all possible perspectives; and represented in a hierarchical structure with differing levels of granularity. This paper discusses about bringing all the manifestations of an ontological concept/decision under one umbrella; hence describing the resultant scheme as a unit of knowledge. By representing a concept as a knowledge unit, classical ontology is claimed to be sufficient in dealing with imprecise, vague and heterogeneous knowledge for real-world web applications; and portrays the capability to acquire fresh knowledge through its thorough interaction with the external world in a given working environment.
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Jain, S., and Jain, N. K. (2008). A generalized knowledge representation system for context sensitive reasoning: Generalized HCPRs System. Artificial Intelligence Review, 30(1–4), 39.
Shandilya S.K., Opinion Extraction and Classification of Reviews from Web Documents (2009). IEEE International Advance Computing Conference (IACC 2009).
A. Patel, and S. Jain, “Present and future of semantic web technologies: a research statement”, Int. J. Comput. Appl., 2019.
Patel, A., and Jain, S. (2018). Formalisms of Representing Knowledge. Procedia Computer Science, 125, 542–549.
Michalski, R. S., and Winston, P. H. (1986). Variable precision logic. Artificial intelligence, 29(2), 121–146.
Bharadwaj, K. K., and Jain, N. K. (1992). Hierarchical censored production rules (HCPRs) system. Data and knowledge engineering, 8(1), 19–34.
Jain, N. K., Bharadwaj, K. K., and Marranghello, N. (1999). Extended hierarchical censored production rules (EHCPRs) system: An approach toward generalized knowledge representation. Journal of Intelligent Systems, 9(3-4), 259–295.
Jain, N. K., and Jain, S. (2013). Live multilingual thinking machine. Journal of Experimental & Theoretical Artificial Intelligence, 25(4), 575–587.
Jain, S., and Jain, N. K. (2014, March). A Globalized Intelligent System. In Computing for Sustainable Global Development (INDIACom), 2014 International Conference on (pp. 425-431). IEEE.
Horrocks, I. (2005, October). Owl: A description logic based ontology language. In International Conference on Principles and Practice of Constraint Programming (pp. 5–8). Springer, Berlin, Heidelberg.
Bobillo, F., and Straccia, U. (2011). Fuzzy ontology representation using OWL 2. International Journal of Approximate Reasoning, 52(7), 1073–1094.
A. Patel, and S. Jain, “An intelligent resource manager over terrorism knowledge base”, Recent Patents on Computer Science, 2019.
Patel, A. and Jain, S. (2019). “A Partition Based Approach for large Scale Ontology Matching”, Recent Patents on Engineering,
Jain, S., and Jain, N. K. (2010). Acquiring knowledge in extended hierarchical censored production rules (EHCPRS) system. International Journal of Artificial Life Research (IJALR), 1(4), 10–28.
Sowa, J. F. (2006). Semantic networks. Encyclopedia of Cognitive Science.
Marchetti, A., Ronzano, F., Tesconi, M., and Minutoli, M. (2008). Formalizing Knowledge by Ontologies: OWL and KIF. Relatórioapresenta-doL’Istituto di Informatica e Telematica (IIT).ConsiglioNazionaledelle Ricerche (CNR). Italia.
Ian Horrocks, Peter F. Patel-Schneider, Knowledge Representation and Reasoning on the Semantic Web : OWL, Published 2010.
World Wide Web Consortium. (2012). OWL 2 web ontology language document overview.
Taye, M. M. (2011). Web-Based Ontology Languages and its Based Description Logics. The Research Bulletin of Jordan ACM, 2, 1–9.
D.L. McGuinness, R. Fikes, L.A. Stein, and J.A. Hendler, “DAML-ONT: An Ontology Language for the Semantic Web”, In Proceedings of Spinning the Semantic Web, 2003, pp. 65–93.
McGuinness, D. L., Fikes, R., Hendler, J., and Stein, L. A. (2002). DAML+ OIL: an ontology language for the Semantic Web. IEEE Intelligent Systems, 17(5), 72–80.
Jain, V., and Singh, M. (2013). Ontology based information retrieval in semanticweb:Asurvey. International Journal of Information Technology and Computer Science (IJITCS), 5(10), 62.
Stoilos, G., Simou, N., Stamou, G., and Kollias, S. (2006). Uncertainty and the semantic web. IEEE Intelligent Systems, 21(5), 84–87.
Bobillo, F., and Straccia, U. (2011). Fuzzy ontology representation using OWL 2. International Journal of Approximate Reasoning, 52(7), 1073–1094.
Milea, V., Frasincar, F., and Kaymak, U. (2012). tOWL: a temporal web ontology language. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(1), 268–281.
Thomas, E., Pan, J. Z., andRen,Y.(2010, May). TrOWL: Tractable OWL 2 reasoning infrastructure. In Extended Semantic Web Conference (pp. 431–435). Springer, Berlin, Heidelberg.
Lee, C. S., Wang, M. H., and Hagras, H. (2010). A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation. IEEE Transactions on Fuzzy Systems, 18(2), 374–395.
Ausín, D., Castanedo, F., and López-de-Ipina, D. (2012, December). TURAMBAR: An approach to deal with uncertainty in semantic environments. In International Workshop on Ambient Assisted Living (pp. 329–337). Springer, Berlin, Heidelberg.
Bobillo, F., andStraccia, U. (2011). Fuzzy ontology representation using OWL 2. International Journal of Approximate Reasoning, 52(7), 1073–1094.
Mallik, A., Ghosh, H., Chaudhury, S., and Harit, G. (2013). MOWL: An ontology representation language for web-based multimedia applications. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 10(1), 8.
Ding, Z., and Peng, Y. (2004, January). A probabilistic extension to ontology language OWL. In System Sciences, 2004. Proceedings of the 37th Annual Hawaii international conference on System Sciences.
Stoilos, G., Stamou, G., and Pan, J. Z. (2010). Fuzzy extensions of OWL: Logical properties and reduction to fuzzy description logics. International Journal of Approximate Reasoning, 51(6), 656–679.
Lera, I., Juiz, C., and Puigjaner, R. (2010, February). Owl-m extension for semantic representations of ontology alignments. In 2010 International Conference on Complex, Intelligent and Software Intensive Systems (pp. 956–961). IEEE.
Hewahi, N. M. (2018). A Security Technique for Censor Production Rules-based Systems. KnE Engineering, 3(7), 1–10.
Hewahi, N. M. (2018). Concept Based Censor Production Rules. International Journal of Decision Support System Technology (IJDSST), 10(1), 59–67.
Jain, S., and Jain, N. K. (2012). Learning techniques in extended hierarchical censored production rules (EHCPRs) system. Artificial Intelligence Review, 38(2), 97–117.
Jain, S., Jain, N. K., and Goel, C. K. (2009). Reasoning in EHCPRs system. Int. J. Open Problems Compt. Math, 2(2).
Jain, S., and Jain, N. K. (2010). Representation of defaults and constraints in EHCPRs system: an implementation. International Journal of Adaptive and Innovative Systems, 1(2), 105–120.