Abstract
An Ontology is an absolute formal conceptualization of some realm of significance. Nowadays Ontologies play a vibrant part in Information Architecture, Biomedical Informatics, Electronic commerce, Software Engineering, Semantic Web, Knowledge management, Artificial Intelligence and etc. Huge number of Ontologies and extensive variety of Ontologies are available for every single domain. It creates very difficult to maintain and access all the existing Ontologies. Ontology merging is the solution to overcome this kind of problems. Ontology merging is a procedure of fetching two existing Ontologies as input and obtains a newly merged Ontology as output. The merged Ontology will have common concepts and relationships between two Ontologies. This paper presents how two Ontologies can be constructed and merged using Protege and Conexp tools with an example.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, S., Lu, Q., Li, W.: Experiments of ontology construction with formal concept analysis. In: International Joint Conference on Natural Language Processing, pp. 67–75 (2005)
Priya, M., Aswani Kumar, Ch.: A survey of state of the art of ontology construction and merging using formal concept analysis. Indian J. Sci. Technol. 8(24), 2–7 (2015)
Lian, Z.: A tool to support ontology creation based on incremental mini-ontology merging. Data Extraction Research Group, Department of Computer Science, Brigham Young University (2008)
de Bruijn, J., Ehrig, M., Feier, C., Martin-Recuerda, F., Scharffe, F., Weiten, M.: Ontology mediation, merging, and aligning. In: Davies, J., Studer, R., Warren, P. (eds.) Semantic Web Technologies: Trends and Research in Ontology-Based Systems, pp. 102–104. Wiley, New York (2006)
Noy, N.F., Musen, M.A.: PROMPT: algorithm and tool for automated ontology merging and alignment. In: Proceedings of the AAAI 2000, pp. 1–6 (2000)
Malik, S.K., Prakash, N., Rizvi, S.A.M.: Ontology merging using prompt plug-in of protégé in semantic web. In: International Conference on Computational Intelligence and Communication Networks, pp. 476–481 (2010)
Ostrowski, D.A., Schleis, G.M.: Enterprise ontology merging for the semantic web. In: Proceedings of 2008 International Conference on Semantic Web and Web Services, WorldComp 2008, Las Vegas, Nevada, USA, 7–17 July (2008)
Huang, Z., Van Harmelen, F., Teije, A.T., Groot, P., Visser, C.: Reasoning with inconsistent ontologies: a general framework. EU-IST Integrated Project (IP) IST-2003-506826 SEKT (2005)
Stephens, L.M., Gangam, A., Huhns, M.N.: Constructing consensus ontologies for the semantic web: a conceptual approach. World Wide Web: Internet Web Inf. Syst. 7, 421–442 (2004)
Uschold, M., Gruninger, M.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11(2), 93–136 (1996)
Ganter, B., Stumme, G.: Creation and merging of ontology top-levels. In: International Conference on Conceptual Structures, vol. 2746, pp. 131–145, July 2003
Fan, Z., Zlatanova, S.: Exploring ontologies for semantic interoperability of data in emergency response. Appl. Geomat. 3(2), 109–122 (2011)
Gómez-Pérez, A., Fernández-López, M.: Ontological Engineering: With Examples from the Areas of Knowledge Management, E-Commerce and the Semantic Web. Springer, London (2010)
Li, X., Liu, G., Ling, A., Zhan, J., An, N., Li, L., Sha, Y.: Building a practical ontology for emergency response systems. In: 2008 International Conference on Computer Science and Software Engineering, vol. 4, pp. 222–225 (2008)
Aswani Kumar, Ch., Srinivas, S.: Concept lattice reduction using fuzzy k-means clustering. Expert Syst. Appl. 9(1), 2696–2704 (2010)
Aswani Kumar, Ch.: Fuzzy clustering based formal concept analysis for association rule mining. Appl. Artif. Intell. 26(3), 274–301 (2005)
Aswani Kumar, Ch., Dias, S.M., Vieira, N.J.: Knowledge reduction in formal contexts using non-negative matrix factorization. Math. Comput. Simul. 109, 46–63 (2015)
Aswani Kumar, Ch.: Mining association rules using non-negative matrix factorization and formal concept analysis. In: International Conference on Information Processing, vol. 157, no. 1, pp. 31–39 (2011)
Mouliswaran, S.C., Aswani Kumar, Ch., Chandrasekar, C.: Modeling Chinese wall access control using formal concept analysis. In: International Conference on Contemporary Computing and Informatics (IC3I) (2017)
Aswani Kumar, Ch., Srinivas, S.: Concept lattice reduction using fuzzy K-means clustering. Expert Syst. Appl. 37(3), 2696–2704 (2010)
Prem Kumar, S., Aswani Kumar, Ch.: Bipolar fuzzy graph representation of concept lattice. Inf. Sci. 288, 437–448 (2014)
Chunduri, R.K., Aswani Kumar, Ch.: Scalable formal concept analysis algorithm for large datasets using spark. J. Ambient Intell. Humanized Comput. 1–21 (2018)
Chunduri, R.K., Aswani Kumar, Ch.: HaLoop approach for concept generation in formal concept analysis. JIKM 17(3), 1850029 (2018)
Subramanian, C.M., Aswani Kumar, Ch., Chelliah, C.: Role based access control design using three-way formal concept analysis. Int. J. Mach. Learn. Cybern. 9(11), 1807–1837 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Priya, M., Aswani Kumar, C. (2020). Construction and Merging of ACM and ScienceDirect Ontologies. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-16660-1_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16659-5
Online ISBN: 978-3-030-16660-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)