Multimedia Tools and Applications

, Volume 76, Issue 8, pp 10389–10406 | Cite as

A novel approach for automatic text analysis and generation for the cultural heritage domain

  • Francesco Piccialli
  • Fiammetta Marulli
  • Angelo Chianese


Knowledge is information that has been contextualised in a certain domain, to be used or applied. It represents the basic core of our Cultural Heritage and Natural Language provides us with prime versatile means of construing experience at multiple levels of organization. The natural language generation field consists in the creation of texts providing information contained in other kind of sources (numerical data, graphics, taxonomies and ontologies or even other texts), with the aim of making such texts indistinguishable, as far as possible, from those created by humans. On the other hand, the knowledge extraction, basing on text mining and text analysis tasks, as examples of the many applications born from computational linguistic, provides summarization, categorization, topics extractions from textual resources using linguistic concepts, which deal with the imprecision and ambiguity of human language. This paper presents a research activity focused on exploring and scientifically describing knowledge structure and organization involved in textual resources’ generation. Thus, a novel multidimensional model for the representation of conceptual knowledge, is proposed. Furthermore, a real case study in the Cultural Heritage domain is described to demonstrate the effectiveness and the feasibility of the proposed model and approach.


Natural language generation Cultural heritage Text generation Knowledge modeling 


  1. 1.
    AAT, Getty Vocabularies, 2015,
  2. 2.
    Amato F, Chianese A, Mazzeo A, Moscato V, Picariello A, Piccialli F (2013) The talking museum project. Procedia Comput Sci 21:114–121CrossRefGoogle Scholar
  3. 3.
    Androutsopoulos I, Kokkinaki V, Dimitromanolaki A, Calder J, Oberlander J, Not E (2001) Generating multilingual personalized descriptions of museum exhibits – the m-piro project. In: Proceedings of the 29th conference on computer applications and quantitative methods in archaeologyGoogle Scholar
  4. 4.
    Androutsopoulos I, Lampouras G, Galanis D (2013) Generating natural language descriptions from OWL ontologies: the naturalOWL system. J Artif Intell Res 48:671–715MATHGoogle Scholar
  5. 5.
    Bello-Orgaz G, Jung JJ, Camacho D (2016) Social big data: recent achievements and new challenges. Inf Fusion 28:45–59CrossRefGoogle Scholar
  6. 6.
    Chianese A, Piccialli F, Valente I (2015) Smart environments and cultural heritage: a novel approach to create intelligent cultural spaces. J Locat Based Serv:209–334Google Scholar
  7. 7.
    Chianese A, Piccialli F (2016) A smart system to manage the context evolution in the cultural heritage domain. Comput Electr Eng. doi: 10.1016/j.compeleceng.2016.02.008
  8. 8.
    Chianese A, Marulli F, Piccialli F, Benedusi P, Jung JE (2016) An associative engines based approach supporting collaborative analytics in the internet of cultural things, future generation computing systems. Elsevier. doi: 10.1016/j.future.2016.04.015
  9. 9.
    EAGLES Project, Natural Language Generation,, 1996
  10. 10.
    Feldman R (2002) Epistemology. Prentice HallGoogle Scholar
  11. 11.
    Fodors JA (1975) The language of thought. Harvard University Press, Cambridge, p 214Google Scholar
  12. 12.
    Galanis D, Karakatsiotis, Androutsopoulos G (2008) How to install NaturalOWL,
  13. 13.
    Hobbes T (1651) Leviathan. Clarendon Press, OxfordGoogle Scholar
  14. 14.
    Hobbes T (1969) Elements of law, natural and political. Routledge, p 186Google Scholar
  15. 15.
    Jena, Apache JENA API, 2015,
  16. 16.
    JYT, Jython: Python for the Java Platform, 2015,
  17. 17.
    LoBue P, Yates A (2012) Types of common-sense knowledge needed for recognizing textual entailment. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 329–334Google Scholar
  18. 18.
    Malakasiotis P (2011) Paraphrase and textual entailment recognition and generation. PhD thesis, Department of Informatics, Athens University of Economics and BusinessGoogle Scholar
  19. 19.
    Marulli F (2015) IoT to enhance understanding of Cultural Heritage: Fedro authoring platform, artworks telling their fables. In: Proceedings of 1st EAI international conference on future access enablers of ubiquitous and intelligent infrastructures (FABULOUS2015). SpringerGoogle Scholar
  20. 20.
    MWN, MultiWordNet, 2015,
  21. 21.
    NLTK, Natural Language Toolkit, 2015,
  22. 22.
    Ramirez C, Valdes B A general knowledge representation model of concepts. In: Ramirez C (ed) Advances in knowledge representation. ISBN: 978-953-51-0597-8, InTechGoogle Scholar
  23. 23.
    Reiter E, Dale R (1997) Building applied natural language generation systems. Nat Lang Eng 3:57–87CrossRefGoogle Scholar
  24. 24.
    WDNET, WordNet, a lexical database for English, 2015,

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Francesco Piccialli
    • 1
  • Fiammetta Marulli
    • 1
  • Angelo Chianese
    • 1
  1. 1.University of Naples Federico IINaplesItaly

Personalised recommendations