Competency Questions (CQs) are a form of ontology functional requirements
expressed as natural language questions. Inspecting CQs together with the
axioms in an ontology provides critical insights into the intended scope and
applicability of the ontology. CQs also underpin a number of tasks in the
development of ontologies e.g. ontology reuse, ontology testing, requirement
specification, and the definition of patterns that implement such requirements.
Although CQs are integral to the majority of ontology engineering
methodologies, the practice of publishing CQs alongside the ontological
artefacts is not widely observed by the community. In this context, we present
an experiment in retrofitting CQs from existing ontologies. We propose
RETROFIT-CQs, a method to extract candidate CQs directly from ontologies using
Generative AI. In the paper we present the pipeline that facilitates the
extraction of CQs by leveraging Large Language Models (LLMs) and we discuss its
application to a number of existing ontologies.

提出一种使用生成型人工智能直接从本体中提取候选复杂问题的方法 RETROFIT-CQs，并讨论其在现有本体中的应用。

对现有本体进行能力问题改进的实验

An Experiment in Retrofitting Competency Questions for Existing  Ontologies

We present the Frame-based ontology Design Outlet (FrODO), a novel method and
tool for drafting ontologies from competency questions automatically.
Competency questions are expressed as natural language and are a common
solution for representing requirements in a number of agile ontology
engineering methodologies, such as the eXtreme Design (XD) or SAMOD. FrODO
builds on top of FRED. In fact, it leverages the frame semantics for drawing
domain-relevant boundaries around the RDF produced by FRED from a competency
question, thus drafting domain ontologies. We carried out a user-based study
for assessing FrODO in supporting engineers for ontology design tasks. The
study shows that FrODO is effective in this and the resulting ontology drafts
are qualitative.

本文介绍了一种基于框架本体论设计输出 (FrODO) 的新方法和工具，通过自动化将能力问题表达为自然语言，构建领域本体，该方法利用 FRED 的框架语义来绘制与领域相关的边界。研究表明，这种方法对于工程师进行本体设计任务是有效的，因为生成的本体草案质量是优良的。

使用 FrODO 从能力问题自动草拟本体

Automatically Drafting Ontologies from Competency Questions with FrODO

During cluster analysis domain experts and visual analysis are frequently
relied on to identify the optimal clustering structure. This process tends to
be adhoc, subjective and difficult to reproduce. This work shows how competency
questions can be used to formalise expert knowledge and application
requirements for context specific evaluation of a clustering application in the
residential energy consumption sector.

本文介绍了如何使用能力问题来规范专家知识和应用需求，以在住宅能源消费领域对聚类应用进行上下文特定评估。