An ontology formalises a number of dependent and related concepts in a domain, encapsulated as a terminology. Manually defining such terminologies is a complex, time-consuming and error-prone task. Thus, there is great interest for strategies to learn terminologies automatically. However, most of the existing approaches induce a single concept definition at a time, disregarding dependencies that may exist among the concepts. As a consequence, terminologies that are difficult to interpret may be induced. Thus, systems capable of learning all concepts within a single task, respecting their dependency, are essential for reaching concise and readable ontologies. In this paper, we tackle this issue presenting three terminology learning strategies that aim at finding dependencies among concepts, before, during or after they have been defined. Experimental results show the advantages of regarding the dependencies among the concepts to achieve readable and concise terminologies, compared to a system that learns a single concept at a time. Moreover, the three strategies are compared and analysed towards discussing the strong and weak points of each one.

中文翻译：

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通过三个视角学习描述逻辑中的多个概念

本体将领域中的许多相关和相关概念形式化，封装为术语。手动定义此类术语是一项复杂、耗时且容易出错的任务。因此，人们对自动学习术语的策略非常感兴趣。然而，大多数现有方法一次只引入一个概念定义，而忽略了概念之间可能存在的依赖关系。因此，可能会引入难以解释的术语。因此，能够在单个任务中学习所有概念并尊重它们的依赖性的系统对于达到简洁易读的本体至关重要。在本文中，我们解决了这个问题，提出了三种术语学习策略，旨在发现概念之间的依赖关系，在定义之前、期间或之后。实验结果表明，与一次学习单个概念的系统相比，考虑概念之间的依赖关系以实现可读和简洁的术语的优势。此外，对三种策略进行了比较和分析，以讨论每种策略的优缺点。