Visual Justifications for Ontologies

本体的视觉论证

• 批准号: EP/M016323/2
• 负责人: Peter Chapman
• 金额: $ 6.81万
• 依托单位: Edinburgh Napier University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM016323%2F2
• 关键词: Visual; Justifications; Ontologies

Ontologies are a way of reasoning about data in an efficient manner. Ontologies are increasingly prevalent in a range of applications, including the Semantic Web, medicine and law. The development and maintenance of ontologies are skilled tasks requiring knowledge of logical reasoning and symbolic notations. However, the wide variety of stakeholders for each ontology may not have the necessary skill set to perform ontology engineering effectively. Given the critical systems in which ontologies are used, it is of paramount importance that the ontologies encode exactly the information intended. Ontologies containing errors, called incoherent ontologies, undergo debugging or repair by an ontology engineer. Extant ontology reasoners provide a justification for the incoherence. However, interpreting the justification is a non-trivial and difficult task. Even if the engineer understands the domain of the ontology, for example medicine, and the symbolic notation in which the justification is represented, they could still struggle to debug the ontology. It is especially difficult to debug the ontology without unintentionally removing intended behaviour. This project will use concept diagrams to visualise justifications to reduce the burden on the ontology engineer. Using concept diagrams will help both the understanding of the problem and suggest appropriate repairs to the ontology. The project will provide a number of different visualisations of common bugs in ontologies and empirically test the effectiveness of each. Through this process, the project team will be able to develop effective visual justifications. Using real-world examples of ontologies for data privacy supplied by the project partner HERE (a Nokia company) the visual justifications will then be tested against equivalent symbolic and natural language justifications.

本体是以一种有效的方式对数据进行推理的方法。本体在一系列应用中越来越流行，包括语义网、医学和法律。本体的开发和维护是需要逻辑推理和符号符号知识的熟练任务。然而，每个本体的各种涉众可能没有必要的技能集来有效地执行本体工程。对于使用本体的关键系统，本体准确地编码所需的信息是至关重要的。包含错误的本体，称为不连贯的本体，由本体工程师调试或修复。现存的本体论推理者为这种不一致性提供了理由。然而，解释这一理由是一项重要而艰巨的任务。即使工程师理解本体论的领域，例如医学，以及表示证明的符号符号，他们仍然可能难以调试本体论。在不无意中删除预期行为的情况下调试本体尤其困难。这个项目将使用概念图来可视化证明，以减轻本体工程师的负担。使用概念图将有助于理解问题并对本体提出适当的修复建议。该项目将提供本体中常见错误的许多不同的可视化，并对每个可视化的有效性进行实证测试。通过这个过程，项目团队将能够开发出有效的视觉证明。使用项目合作伙伴HERE（一家诺基亚公司）提供的数据隐私本体的真实示例，然后将对视觉证明与等效的符号和自然语言证明进行测试。

项目成果

Antipattern Comprehension: An Empirical Evaluation

反模式理解：实证评估

• 发表时间: 2016
• 作者: T. Hou

Implementation Planning for Integrating Depression Screening in Diabetes Mellitus and HIV Clinics in Botswana.

博茨瓦纳糖尿病和艾滋病毒诊所整合抑郁症筛查的实施计划。

• DOI: 10.1007/978-3-319-91376-6_62
• 发表时间: 2022
• 期刊: Global implementation research and applications
• 作者: Molebatsi K

Measuring perceived clutter in concept diagrams

• DOI: 10.1109/vlhcc.2016.7739661
• 发表时间: 2016
• 期刊: 2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
• 作者: Tie Hou;P. Chapman;Ian Oliver