Ontology Learning Approach Based on Analysis of the Context and Metadata of a Weakly Structured Content

  • Dmitry VolchekEmail author
  • Aleksei Romanov
  • Dmitry Mouromtsev
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1159)


This article describes ontology learning approach based on the analysis of metadata and the context of weakly structured content. Today, there is a paradigm shift in ontological engineering. It consists of the transition from manual to automatic or semi-automatic design. This approach is called ontology learning. When an author creates a document, one holds in one’s head a model of a certain subject area. Then, analyzing the document, it is possible to restore the model of this subject area. This process is called reverse engineering. Current articles describe ontology learning approaches based on content analysis. We propose to use not only the content, but, if it is possible, its metadata and the context for ontology learning purposes. As the main results of the work, we can introduce the model for the joint presentation of content and its metadata in a content management system. To extract the terms, the ensemble method was used, combining the algorithms for extracting terms both with and without contrast corpus. Metadata was used to expand candidates attribute space. In addition, methods for constructing taxonomic relations based on the vector representation of words and non-taxonomic relations by analyzing universal dependencies are described.


Ontology learning MOOC Online courses Semantic technologies Ontology Embeddings 


  1. 1.
    Konstantinova, N., Mitrofanova, O.: Ontology as a knowledge storage system. Portal, Information and Communication Technologies in Education. Accessed 21 Mar 2018
  2. 2.
    Gavrilova, T., Gulyakina, N.: Visual knowledge processing techniques: a brief review. Sci. Tech. Inf. Process. 38(6), 403–408 (2011)CrossRefGoogle Scholar
  3. 3.
    Gavrilova, T., Gladkova, M.: Big data structuring: the role of visual models and ontologies. Procedia Comput. Sci. 31, 336–343 (2014)CrossRefGoogle Scholar
  4. 4.
    Gavrilova, T., Khoroshevsky, V.: Knowledge Bases of Intelligent Systems. Piter, Sankt-Petersburg (2000)Google Scholar
  5. 5.
    Chastikov, A., Gavrilova, T., Belov, D.: Development of expert systems. CLIPS Environment, vol. 608. BHV-Petersburg, Saint-Petersburg (2003)Google Scholar
  6. 6.
    Asim, M.N., Wasim, M., Khan, M.U.G., Mahmood, W., Abbasi, H.M.: A survey of ontology learning techniques and applications. Database 2018, 1–24 (2018)CrossRefGoogle Scholar
  7. 7.
    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Berlin (2012)zbMATHGoogle Scholar
  8. 8.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp. 3111–3119 (2013)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Dmitry Volchek
    • 1
    Email author
  • Aleksei Romanov
    • 1
  • Dmitry Mouromtsev
    • 1
  1. 1.ITMO UniversitySt. PetersburgRussia

Personalised recommendations