Ontology-learning methods were introduced in the knowledge engineering area to automatically build ontologies from natural language texts related to a domain. Despite the initial appeal of these methods, automatically generated ontologies may have errors, inconsistencies, and a poor design quality, all of which must be manually fixed, in order to maintain the validity and usefulness of automated output. In this work, we propose a methodology to assess ontologies quality (quantitatively and graphically) and to fix ontology inconsistencies minimizing design defects. The proposed methodology is based on the Deming cycle and is grounded on quality standards that proved effective in the software engineering domain and present high potential to be extended to knowledge engineering quality management. This paper demonstrates that software engineering quality assessment approaches and techniques can be successfully extended and applied to the ontology-fixing and quality improvement problem. The proposed methodology was validated in a testing ontology, by ontology design quality comparison between a manually created and automatically generated ontology.

在知识工程领域中引入了本体学习方法，以根据与领域相关的自然语言文本自动构建本体。尽管这些方法最初具有吸引力，但自动生成的本体可能会存在错误，不一致和设计质量差的问题，所有这些都必须手动修复，以保持自动输出的有效性和实用性。在这项工作中，我们提出了一种方法（以定量和图形方式）评估本体质量，并修复本体不一致性，从而最大程度地减少了设计缺陷。所提出的方法基于Deming周期，并基于质量标准，该质量标准在软件工程领域被证明是有效的，并且具有扩展到知识工程质量管理的巨大潜力。本文证明，软件工程质量评估方法和技术可以成功扩展，并应用于本体修复和质量改进问题。通过在手动创建和自动生成的本体之间进行本体设计质量比较，在测试本体中验证了所提出的方法。