Expressive Ontology Learning as Neural Machine Translation

Automated ontology learning from unstructured textual sources has been proposed in literature as a way to support the difficult and time-consuming task of knowledge modeling for semantic applications. In this paper we propose a system, based on a neural network in the encoder-decoder configuration, to translate natural language definitions into Description Logics formulae through syntactic transformation. The model has been evaluated to asses its capacity to generalize over different syntactic structures, tolerate unknown words, and improve its performance by enriching the training set with new annotated examples. The results obtained in our evaluation show how approaching the ontology learning problem as a neural machine translation task can be a valid way to tackle long term expressive ontology learning challenges such as language variability, domain independence, and high engineering costs.