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 formulæ through syntactic transformation. The model has been evaluated to assess 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.

文献中已经提出了从非结构化文本源进行自动本体学习的一种方法，以支持语义应用知识建模的困难且耗时的任务。在本文中，我们提出了一种基于神经网络的编码器-解码器配置系统，该系统可通过语法转换将自然语言定义转换为描述逻辑公式。该模型已经过评估，可以评估其在不同句法结构上进行概括，容忍未知单词以及通过使用新的带注释的示例丰富训练集来提高其性能的能力。我们在评估中获得的结果表明，将本体学习问题作为神经机器翻译任务来处理是解决长期表达本体学习挑战（例如语言可变性，