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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Ontology Learning in the Deep

verfasst von : Giulio Petrucci, Chiara Ghidini, Marco Rospocher

Erschienen in: Knowledge Engineering and Knowledge Management

Verlag: Springer International Publishing

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Abstract

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.

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Fußnoten
3
Possibly after resolving anaphora, coreference, or other linguistic phenomena.
 
4
The goal of our work is to show that the ontology learning task can be tackled using neural network based models trained in a end-to-end fashion. Assessing the best neural network architecture to implement statistical learning for this task is beyond the scope of our work.
 
5
More precisely, the constructs considered correspond to the \(\mathcal {ALCQ}\) Description Logic, a well-known, expressive extension of \(\mathcal {ALC}\) with qualified number restrictions.
 
6
The list of all the sentence and formula templates is available here: https://​drive.​google.​com/​file/​d/​0B_​FaCg6LWgw5Z0UxM2​N1dTYwYkU.
 
7
The several training, evaluation and test sets used in the experiments are available here: https://​drive.​google.​com/​file/​d/​0B_​FaCg6LWgw5ZnBkSE​VONWx2YW8.
 
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Metadaten
Titel
Ontology Learning in the Deep
verfasst von
Giulio Petrucci
Chiara Ghidini
Marco Rospocher
Copyright-Jahr
2016
Buch
Knowledge Engineering and Knowledge Management

Print ISBN: 978-3-319-49003-8

Electronic ISBN: 978-3-319-49004-5

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-49004-5_31