Developing a Fuzzy Knowledge Base and Filling It with Knowledge Extracted from Various Documents

  • Nadezhda Yarushkina
  • Vadim Moshkin
  • Aleksey FilippovEmail author
  • Gleb Guskov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)


The article describes the process of developing a fuzzy knowledge base. The content of the fuzzy knowledge base is the result of extracting knowledge from the set of documents by subject area. Set of documents consists of the wiki-resources, UML-diagrams, documents and source code of projects. Knowledge base based on the graph database Neo4j. An attempt to implement the mechanism of inference by the contents of a graph database was made. This mechanism is used to generate the screen forms of the user interface dynamically. The contexts allow representing the content of the fuzzy knowledge base in space and time. Each space context is assigned a linguistic label, for example, low, middle, high. This label determines the competence of the expert in the given subject area. Time contexts allow storing the history of the knowledge base content changes. It allows returning to a specific state of the contents of the knowledge base.


Ontology Fuzzy knowledge base Context analysis Subject area Graph database 



The study was supported by the Ministry of Education and Science of the Russian Federation in the framework of the project No. 2.1182.2017/4.6. Development of methods and means for automation of production and technological preparation of aggregate-assembly aircraft production in the conditions of a multi-product production program.


  1. 1.
    Berant, J., Chou, A., Frostig, R., Liang, P.: Semantic parsing on freebase from question-answer pairs. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1533–1544 (2013)Google Scholar
  2. 2.
    Bianchini, D., De Antonellis, V., Pernici, B., Plebani, P.: Ontology-based methodology for e-service discovery. Inf. Syst. 31(4), 361–380 (2005)Google Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Carvalho, N.R., Almeida, J.J., Henriques, P.R., Pereira, M.J.V.: Conclave: ontology-driven measurement of semantic relatedness between source code elements and problem domain concepts. In: Murgante, B., et al. (eds.) ICCSA 2014. LNCS, vol. 8584, pp. 116–131. Springer, Cham (2014). Scholar
  6. 6.
    Dentler, K., Cornet, R., ten Teije, A., de Keizer, N.: Comparison of reasoners for large ontologies in the OWL 2 EL profile. Semant. Web 2, 71–87 (2011)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    Farid, D.M., Al-Mamun, M.A., Manderick, B., Nowe, A.: An adaptive rule-based classifier for mining big biological data. Expert Syst. Appl. 64, 305–316 (2016)CrossRefGoogle Scholar
  9. 9.
    Almeida Ferreira, D., Silva, A.: UML to OWL mapping overview an analysis of the translation process and supporting tools. In: 7th Conference of Portuguese Association of Information Systems, pp. 2536–2549 (2013)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Guarino, N., Musen, M.A.: Ten years of applied ontology. Appl. Ontol. 10(3–4), 169–170 (2015)CrossRefGoogle Scholar
  12. 12.
    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). Scholar
  13. 13.
    Gruber, T.: Ontology. Accessed 10 Jan 2018
  14. 14.
    Guskov, G., Namestnikov, A., Yarushkina, N.: Approach to the search for similar software projects based on the UML ontology. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Vasileva, M., Sukhanov, A. (eds.) IITI 2017. AISC, vol. 680, pp. 3–10. Springer, Cham (2018). Scholar
  15. 15.
    Hattori, S., Takama, Y.: Recommender system employing personal-value-based user model. J. Adv. Comput. Intell. Intell. Inform. (JACIII) 18(2), 157–165 (2014)CrossRefGoogle Scholar
  16. 16.
    Koukias, A., Nadoveza, D., Kiritsis, D.: An ontology-based approach for modelling technical documentation towards ensuring asset optimisation. Int. J. Prod. Lifecycle Manag. 8(1), 24–45 (2015)CrossRefGoogle Scholar
  17. 17.
    Neo4j. Accessed 10 Jan 2018
  18. 18.
    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)CrossRefGoogle Scholar
  19. 19.
    Pellet Framework. Accessed 10 Jan 2018
  20. 20.
    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)CrossRefGoogle Scholar
  21. 21.
    Renu, R.S., Mocko, G., Koneru, A.: Use of big data and knowledge discovery to create data backbones for decision support systems. Procedia Comput. Sci. 20, 446–453 (2013)CrossRefGoogle Scholar
  22. 22.
    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)CrossRefGoogle Scholar
  23. 23.
    Ruy, F.B., Reginato, C.C., Santos, V.A., Falbo, R.A., Guizzardi, G.: Ontology engineering by combining ontology patterns. In: Johannesson, P., Lee, M.L., Liddle, S.W., Opdahl, A.L., López, Ó.P. (eds.) ER 2015. LNCS, vol. 9381, pp. 173–186. Springer, Cham (2015). Scholar
  24. 24.
    Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge unifying WordNet and Wikipedia. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697–706 (2007)Google Scholar
  25. 25.
    SWRL: A Semantic Web Rule Language Combining OWL and RuleML. Accessed 20 Jan 2018
  26. 26.
    Wongthongtham, P., Pakdeetrakulwong, U., Marzooq, S.H.: Ontology annotation for software engineering project management in multisite distributed software development environments. In: Mahmood, Z. (ed.) Software Project Management for Distributed Computing, pp. 315–343. Springer, Heidelberg (2017). Scholar
  27. 27.
    Yarushkina, N., Filippov, A., Moshkin, V.: 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.) CIT&DS 2017. CCIS, vol. 754, pp. 62–72. Springer, Heidelberg (2017). Scholar
  28. 28.
    Zarubin, A., Koval, A., Filippov, A., Moshkin, V.: Application of syntagmatic patterns to evaluate answers to open-ended questions. In: Kravets, A., Shcherbakov, M., Kultsova, M., Groumpos, P. (eds.) CIT&DS 2017, vol. 754, pp. 150–162. Springer, Heidelberg (2017). Scholar
  29. 29.
    Zedlitz, J., Jörke, J., Luttenberger, N.: From UML to OWL 2. In: Lukose, D., Ahmad, A.R., Suliman, A. (eds.) Proceedings of Knowledge Technology. CCIS, vol. 295, pp. 154–163. Springer, Heidelberg (2012). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Ulyanovsk State Technical UniversityUlyanovskRussian Federation

Personalised recommendations