Ontology Learning in the Deep

Conference paper

DOI: 10.1007/978-3-319-49004-5_31

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10024)
Cite this paper as:
Petrucci G., Ghidini C., Rospocher M. (2016) Ontology Learning in the Deep. In: Blomqvist E., Ciancarini P., Poggi F., Vitali F. (eds) Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science, vol 10024. Springer, Cham


Recent developments in the area of deep learning have been proved extremely beneficial for several natural language processing tasks, such as sentiment analysis, question answering, and machine translation. In this paper we exploit such advances by tailoring the ontology learning problem as a transductive reasoning task that learns to convert knowledge from natural language to a logic-based specification. More precisely, using a sample of definitory sentences generated starting by a synthetic grammar, we trained Recurrent Neural Network (RNN) based architectures to extract OWL formulae from text. In addition to the low feature engineering costs, our system shows good generalisation capabilities over the lexicon and the syntactic structure. The encouraging results obtained in the paper provide a first evidence of the potential of deep learning techniques towards long term ontology learning challenges such as improving domain independence, reducing engineering costs, and dealing with variable language forms.

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Giulio Petrucci
    • 1
    • 2
  • Chiara Ghidini
    • 1
  • Marco Rospocher
    • 1
  1. 1.FBK-irstTrentoItaly
  2. 2.University of TrentoTrentoItaly

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