OWL‐based acquisition and editing of computer‐interpretable guidelines with the CompGuide editor

Computer‐Interpretable Guidelines (CIGs) are the dominant medium for the delivery of clinical decision support, given the evidence‐based nature of their source material. Therefore, these machine‐readable versions have the ability to improve practitioner performance and conformance to standards, with availability at the point and time of care. The formalisation of Clinical Practice Guideline knowledge in a machine‐readable format is a crucial task to make it suitable for the integration in Clinical Decision Support Systems. However, the current tools for this purpose reveal shortcomings with respect to their ease of use and the support offered during CIG acquisition and editing. In this work, we characterise the current landscape of CIG acquisition tools based on the properties of guideline visualisation, organisation, simplicity, automation, manipulation of knowledge elements, and guideline storage and dissemination. Additionally, we describe the CompGuide Editor, a tool for the acquisition of CIGs in the CompGuide model for Clinical Practice Guidelines that also allows the editing of previously encoded guidelines. The Editor guides the users throughout the process of guideline encoding and does not require proficiency in any programming language. The features of the CIG encoding process are revealed through a comparison with already established tools for CIG acquisition.     

Automating application software generation

Many approaches have been proposed to enhance software productivity and reliability. These approaches typically fall into three categories: the engineering approach, the formal approach and the knowledge-based approach. But the optimal gain in software productivity cannot be obtained if one relies on only one of these approaches. This paper describes the work in knowledge-based software engineering conducted by the authors for the past 10 years. The final goal of the research is to develop a paradigm for software engineering which integrates the three approaches mentioned above. A knowledge-based tool which can support the whole process of software development is provided in this paper.     

On the ontological expressiveness of information systems analysis and design grammars

Abstract. Information systems analysis and design (ISAD) methodologies provide facilities for describing existing or conceived real-world systems. These facilities are ontologically expressive if they are capable of describing all real-world phenomena completely and clearly. In this paper we formally examine the notion of the ontological expressiveness of a grammar and discuss some of its implications for the design and use of ISAD methodologies. We identify some generic ways in which ontological expressiveness may be undermined in a grammar and some potential consequences of these violations. We also examine ontological expressiveness within the context of some other desirable features that might be considered in the design of ISAD methodologies.     

基于本体的网络调查问卷生成系统OntoSurvey

网络调查中一个重要的组成部分就是供给被调查者填写的问卷。论文基于本体[1]的思想,设计了一种新型的网络调查问卷生成平台,称为OntoSurvey。OntoSurvey的目标是对各种目的的网络调查活动提供支持,消除制约网络调查发展的技术因素中的一些问题,如数据格式不一致、专业技术之间融合度低等等。实例分析表明,OntoSurvey在技术上是可行的,而且具有目前常规网络调查问卷生成平台所没有的多种优点。     

面向OWL的联合作战管理语言本体开发方法

作战管理语言是一种能够无歧义指挥命令部队和装备进行军事行动和提供态势感知、共享作战行动的语言,其公开标准为联合作战管理语言。联合作战管理语言产品开发组制定了三个阶段开发语言标准,包括数据模型、形式化结构（语法）和形式化语义（本体）,目前的研究主要集中在前两个阶段,对实现概念交互的第三阶段的相关研究还比较欠缺。在总结相关对作战管理语言本体研究的基础上,提出了针对联合作战管理语言本体面向联合作战的一种应用框架。通过对炮兵打击任务分配的实际应用,验证了该方法的合理性和有效性,同时显示了本体在军事领域具有相当严格可靠的模型描述能力。     

Personalized course generation and evolution based on genetic algorithms

Online learners are individuals,and their learning abilities,knowledge,and learning performance differ substantially and are ever changing.These individual characteristics pose considerable challenges to online learning courses.In this paper,we propose an online course generation and evolution approach based on genetic algorithms to provide personalized learning.The courses generated consider not only the difficulty level of a concept and the time spent by an individual learner on the concept,but also the changing learning performance of the individual learner during the learning process.We present a layered topological sort algorithm,which converges towards an optimal solution while considering multiple objectives.Our general approach makes use of the stochastic convergence of genetic algorithms.Experimental results show that the proposed algorithm is superior to the free browsing learning mode typically enabled by online learning environments because of the precise selection of learning content relevant to the individual learner,which results in good learning performance.