Abstract
The article considers the architecture of the technological platform designated for construction of the knowledge base (KB) by integrating a set of logical rules with fuzzy ontologies. Development of integration methods for a set of logical rules and fuzzy ontologies are necessary for decision support process. The KB represents the storage of knowledge and contexts of different problem areas (PrA). The PrA ontology context is a specific state of the KB content than can be chosen from a set of the ontology states. The state was obtained as a result of either versioning or constructing the KB content from different points of views.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rubiolo, M., Caliusco, M.L., Stegmayer, G., Coronel, M., Fabrizi, M.G.: Knowledge discovery through ontology matching: an approach based on an artificial neural network model. Inf. Sci. 194, 107–119 (2012)
Renu, R.S., Mocko, G., Koneru, A.: Use of big data and knowledge discovery to create data backbones for decision support systems. Proc. Comput. Sci. 20, 446–453 (2013)
Ltifi, H., Kolski, C., Ayed, M.B., Alimi, A.M.: A human-centred design approach for developing dynamic decision support system based on knowledge discovery in databases. J. Decis. Syst. 22, 69–96 (2013)
Rajpathak, D., Chougule, R., Bandyopadhyay, P.: A domain-specific decision support system for knowledge discovery using association and text mining. Knowl. Inf. Syst. 31, 405–432 (2012)
Bobillo, F., Straccia, U.: FuzzyDL: an expressive fuzzy description logic reasoner. In: Proceedings of the 17th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008), pp. 923–930. IEEE Computer Society (2008)
Gao, M., Liu, C.: Extending OWL by fuzzy description logic. In: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2005), pp. 562–567. IEEE Computer Society (2005)
Bianchini, D., De Antonellis, V., Pernici, B., Plebani, P.: Ontology-based methodology for e-service discovery. Inf. Syst. 31(4), 361–380 (2006)
Guarino, N., Musen, M.A.: Ten years of applied ontology. Appl. Ontol. 10(3–4), 169–170 (2015)
Guizzardi, G., Guarino, N., Almeida, J.P.A.: Ontological considerations about the representation of events and endurants in business models. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 20–36. Springer, Cham (2016). doi:10.1007/978-3-319-45348-4_2
Falbo, R.A., Quirino, G.K., Nardi, J.C., Barcellos, M.P., Guizzardi, G., Guarino, N.: An ontology pattern language for service modeling. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 321–326 (2016)
Hotho, A., Staab, S., Stumme, G.: Ontologies improve text document clustering. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 541–544 (2003)
Gruber, T.: Ontology. In: Liu, L., Tamer Özsu, M. (eds.) Entry in the Encyclopedia of Database Systems. Springer, New York (2008). doi:10.1007/978-0-387-39940-9_1318
Medche, A.: Ontology Learning for the Semantic Web. Engineering and Computer Science, vol. 665. Kluwer International, Dordrecht (2002). doi:10.1007/978-1-4615-0925-7
Gavrilova, T.A.: Ontologicheskii podkhod k upravleniiu znaniiami pri razrabotke korporativnykh informatsionnykh sistem (The ontological approach to knowledge management in the development of corporate information systems). Novosti iskusstvennogo intellekta (News Artif. Intell.) 2(56), 24–29 (2003)
Namestnikov, A.M, Filippov, A.A., Avvakumova, V.S.: An ontology based model of technical documentation fuzzy structuring. In: CEUR Workshop Proceedings, SCAKD 2016, Moscow, Russian Federation. vol. 1687, pp. 63–74 (2016)
Yarushkina, N., Moshkin, V., Andreev, I., Klein, V., Beksaeva, E.: Hybridization of fuzzy inference and self-learning fuzzy ontology-based semantic data analysis. In: Abraham, A., Kovalev, S., Tarassov, V., Snášel, V. (eds.) Proceedings of the First International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’16). AISC, vol. 450, pp. 277–285. Springer, Cham (2016). doi:10.1007/978-3-319-33609-1_25
Filippov, A.A., Moshkin, V.S., Shalaev, D.O., Yarushkina, N.G.: Uniform ontological data mining platform. In: Golenkov, V., et al. (eds) Open Semantic Technologies of Intelligent Systems (OSTIS-2016): Proceedings of VI International Science Technological Conference (Minsk, 18–20 February 2016), pp. 77-82. BSUIR, Minsk (2016)
SWRL: A semantic web rule language combining OWL and RuleML. https://www.w3.org/Submission/SWRL. Accessed 20 Jan 2017
Bobillo, F., Straccia, U.: Representing fuzzy ontologies in OWL 2. In: Proceedings of the 19th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010), pp. 2695–2700. IEEE Press (2010)
Spring Boot framework. https://projects.spring.io/spring-boot. Accessed 9 Jan 2017
Neo4j. https://neo4j.com/product. Accessed 10 Jan 2017
Greg Wilkins Jetty vs Tomcat: a comparative analysis (2008). http://www.webtide.com/choose/jetty.jsp. Accessed 9 Jan 2017
Representational state transfer. https://en.wikipedia.org/wiki/Representational_state_transfer. Accessed 9 Jan 2017
Pellet framework. https://github.com/stardog-union/pellet. Accessed 10 Jan 2017
Dentler, K., Cornet, R., ten Teije, A., de Keizer, N.: Comparison of reasoners for large ontologies in the OWL 2 EL profile. Semantic Web 2, April 2011, pp. 71–87 (2011)
Acknowledgments
This work was financially supported by the Russian Foundation for Basic Research (Grants No. 16-47-732054 and 16-47-732120).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yarushkina, N., Filippov, A., Moshkin, V. (2017). Development of the Unified Technological Platform for Constructing the Domain Knowledge Base Through the Context Analysis. In: Kravets, A., Shcherbakov, M., Kultsova, M., Groumpos, P. (eds) Creativity in Intelligent Technologies and Data Science. CIT&DS 2017. Communications in Computer and Information Science, vol 754. Springer, Cham. https://doi.org/10.1007/978-3-319-65551-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-65551-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65550-5
Online ISBN: 978-3-319-65551-2
eBook Packages: Computer ScienceComputer Science (R0)