Fuzzy cognitive map software tool for treatment management of uncomplicated urinary tract infection

Uncomplicated urinary tract infection (uUTI) is a bacterial infection that affects individuals with normal urinary tracts from both structural and functional perspective. The appropriate antibiotics and treatment suggestions to individuals suffer of uUTI is an important and complex task that demands a special attention. How to decrease the unsafely use of antibiotics and their consumption is an important issue in medical treatment. Aiming to model medical decision making for uUTI treatment, an innovative and flexible approach called fuzzy cognitive maps (FCMs) is proposed to handle with uncertainty and missing information. The FCM is a promising technique for modeling knowledge and/or medical guidelines/treatment suggestions and reasoning with it. A software tool, namely FCM-uUTI DSS, is investigated in this work to produce a decision support module for uUTI treatment management. The software tool was tested (evaluated) in a number of 38 patient cases, showing its functionality and demonstrating that the use of the FCMs as dynamic models is reliable and good. The results have shown that the suggested FCM-uUTI tool gives a front-end decision on antibiotics’ suggestion for uUTI treatment and are considered as helpful references for physicians and patients. Due to its easy graphical representation and simulation process the proposed FCM formalization could be used to make the medical knowledge widely available through computer consultation systems.     

基于混合推理的知识库的构建及其应用研究

该文提出了基于OWL本体与Prolog规则的平面几何知识库的构建方法,从而可形式化地表示平面几何中丰富的语义信息.一方面,用类型、定义域、值域、分类、属性、实例等本体描述来表达结构化的知识,为领域内概念与概念之间关系的描述提供形式化的语义;另一方面,用Prolog规则来解决本体不能有效表达的诸如属性之间的关系和操作等问题,从而支持复杂关系间的推理.在此基础上,用Protégé和Prolog构建了一个基于本体和规则的平面几何知识库.实验证明:此知识库可实现知识和语义层次上的信息查询,还可进行复杂问题求解,其丰富的语义描述和混合推理能力弥补了传统知识库的不足.     

语义万维网中基于Agent的智能检索框架

语义万维网是目前国际万维网联盟（world wide web consortium，W3C）为了解决因Web上的数据缺少语义信息而难以实现自动化处理的问题所开展的研究项目，其目的是为了对Web上发布的信息实现智能推理和自动化处理。Agent作为一种智能化主体，非常适合语义万维网环境下的各种应用。在语义万维网和智能Agent研究的基础上，综合信息检索、知识表示、Ontology建模等多方面技术，提出并实现了一个基于Ontology实现语义信息检索的多Agent系统框架，该系统包括信息收集、存储、查询和推理4个主要部分。     

Extracting focused knowledge from the semantic web

Ontologies are increasingly being recognized as a critical component in making networked knowledge accessible. Software architectures which can assemble knowledge from networked sources coherently according to the requirements of a particular task or perspective will be at a premium in the next generation of web services. We argue that the ability to generate task-relevant ontologies efficiently and relate them to web resources will be essential for creating a machine-inferencable “semantic web”. The Internet-based multi-agent problem solving (IMPS) architecture described here is designed to facilitate the retrieval, restructuring, integration and formalization of task-relevant ontological knowledge from the web. There are rich structured and semi-structured sources of knowledge available on the web that present implicit or explicit ontologies of domains. Knowledge-level models of tasks have an important role to play in extracting and structuring useful focused problem-solving knowledge from these web sources. IMPS uses a multi-agent architecture to combine these models with a selection of web knowledge extraction heuristics to provide clean syntactic integration of ontological knowledge from diverse sources and support a range of ontology merging operations at the semantic level. Whilst our specific aim is to enable on-line knowledge acquisition from web sources to support knowledge-based problem solving by a community of software agents encapsulating problem-sloving inferences, the techniques described here can be applied to more general task-based integration of knowledge from diverse web sources, and the provision of services such as the critical comparison, fusion, maintenance and update of both formal informal ontologies.     

N-SHOQ(D):描述逻辑SHOQ(D)的一个非单调扩展

描述逻辑SHOQ(D)给出了Web本体语言DAML OIL的语义,但SHOQ(D)只能处理严格成立的完备知识,不能处理在实际情况中经常出现的不完备知识．对描述逻辑SHOQ(D)进行扩展,提出了能够处理不完备知识的非单调描述逻辑N—SHOQ(D)．给出了N—SHOQ(D)的语法和语义,定义了N—SHOQ(D)中的蕴涵推理关系,研究了N—SHOQ(D)所具有的性质．N—SHOQ(D)为扩展DAML OIL语言到能够处理不完备知识的情形提供了语义支持．     

A brain-inspired fuzzy semantic memory model for learning and reasoning with uncertainty

Decision-making pervades the human experience. The human decision process is driven by rational reasoning, which is the capacity to use the faculty of reason to facilitate logical thinking and to derive uncertain but sensible arguments from existing knowledge and the observed fact. Knowledge refers to the accumulation and the continuous neurological organization of information via the repeated exposure to its effective usage. Functionally, a decision support system seeks to provide a systematic and human-like way to data analysis by synthesizing an expert’s knowledge and reasoning capability to support the decision process of the user. However, conventional knowledge engineering and decision support systems often performed poorly when they are applied to problem domains festered with uncertain information, where the quality of the observed data is compromised by measurement noises. This paper presents T2-GenSoFNN, a brain-inspired fuzzy semantic memory model embedded with Type-2 fuzzy logic inference for learning and reasoning with noise-corrupted data. The proposed T2-GenSoFNN model is applied to the modeling of human insulin levels for the proper regulation of blood glucose concentration in diabetes therapy. The results are encouraging.     

基于FMEA和模糊Petri网推理的装备保障过程诊断研究

为满足装备保障过程分析、瓶颈优化的需要,提出基于失效模式影响分析(FMEA)和模糊Petri网推理的装备保障过程诊断方法,通过FMEA建立装备保障过程诊断的因果图,由因果图确定保障过程诊断的推理规则,应用模糊Petri网建立智能的、利于计算机编程实现的保障过程诊断的过程模型。通过研究发现,基于FMEA的规则形成方法便于知识、经验向规则的准确转换提取,模糊Petri网的方法利于将推理过程形式化,实现推理的自动化,提高过程诊断的效率。研究的过程诊断模型和方法已在集群装备保障过程优化决策系统实现中取得较好的效果。     

领域本体驱动下企业联机分析处理系统应用研究

传统联机分析处理(OLAP)系统存在着形式化业务知识参与不足的状况,对深度推理分析造成了制约和局限。为了克服上述缺点,提出一种领域本体驱动的OLAP系统构建方法。首先,通过分析现有本体构建方法的局限性,依托实体类多特征加权相似度判断算法,提出先全局设计后局部抽取的半自动本体构建模式,实现矿山生产领域知识形式化;接着在此基础上,以矿山生产能力关键指标为度量,完成负载业务概念多维本体(MDO)建模;最后,在实际矿山决策系统项目建设中,进行了方法检验。实验结果表明,该方法充分发挥领域本体形式化表达与推理优势,有效整合多源异构信息和明晰多维分析过程,实现内隐知识与关联规则深度挖掘;同时借助高频通用概念视图定义,避免重复维度建模,改进了传统OLAP效能。     

区分3种否定的模糊命题逻辑系统及其应用

在模糊知识表示与推理中,否定信息扮演了一个重要角色.从概念层面上区分了模糊知识中存在的3 种否定关系,即矛盾否定关系、对立否定关系和中介否定关系.为了建立能够完全描述这些不同否定关系的逻辑基础,提出一种区分矛盾否定、对立否定和中介否定的模糊命题逻辑形式系统FLCOM.讨论了FLCOM 特有的性质与意义,给出了FLCOM 的一种语义解释,并证明了可靠性定理.为了表明FLCOM 处理实际问题的适用性,进一步研究了FLCOM在一个模糊决策实例中的应用.具体地,基于FLCOM讨论了决策规则中的模糊命题及其不同否定的区分与形式表示,给出一种确定模糊命题及其不同否定的真值及其真值范围阈值的方法,并采用模糊产生式规则讨论了实例中的模糊推理与决策.从而表明,运用FLCOM 处理具有模糊性并且存在不同否定的实际问题是有效的.     

Using OWL ontologies for adaptive patient information modelling and preoperative clinical decision support

We here present our research and experience regarding the design and implementation of a knowledge-based preoperative assessment decision support system. We discuss generic design considerations as well as the practical system implementation. We developed the system using semantic web technology, including modular ontologies developed in the OWL web ontology language, the OWL Java application programming interface and an automated logic reasoner. We discuss how the system enables to tailor patient information collection according to personalized medical context. The use of ontologies at the core of the system’s architecture permits to efficiently manage a vast repository of preoperative assessment domain knowledge, including classification of surgical procedures, classification of morbidities and guidelines for routine preoperative tests. Logical inference on the domain knowledge according to individual patient’s medical context enables personalized patients’ reports consisting of a risk assessment and clinical recommendations such as relevant preoperative tests.     

EXPERT VS. MANAGEMENT SUPPORT SYSTEMS: SEMANTIC ISSUES

Abstract

Expert systems hold great promise for technical application areas such as medical diagnosis or engineering design. They are, we argue, less promising for management applications. The reason is that managers are not experts in the sense of possessing a formal body of knowledge which they apply. The limitations of artificial intelligence approaches in managerial domains is explained in terms of semantic change, motivating attention toward management (decision( support systems.     

A rule based knowledge transaction model for mobile environments

In this paper, we propose and formalize a rule based knowledge transaction model for mobile environments. Our model integrates the features of both mobile environments and intelligent agents. We use logic programming as a mathematic tool and formal specification method to study knowledge transaction in mobile environments. Our knowledge transaction model has the following major advantages: (1) It can be used for knowledge transaction representation, formalization and knowledge reasoning in mobile environments. (2) It is knowledge oriented and has a declarative semantics inherited from logic programming. (3) It is a formalization that can be applied to general problem domains. We show that our model can be used for knowledge transaction representation, formalization and knowledge reasoning in mobile environments.     

Fuzzy Preference Domains and Their Use for Selecting an Electronic Flight Bag for Flight Crews

A method for the multicriteria evaluation of alternatives based on preferences specified in fuzzy domains is proposed. The concept of fuzzy scales of the criteria and preference domains is formalized. An algorithm for the fuzzy ranking of alternatives is proposed. A free computer implementation of the fuzzy ranking algorithm is available on the portal ws-dss.com of web services for decision support systems. The capabilities of the ranking procedure based on the preferences specified in fuzzy domains are demonstrated using the important problem of selecting an electronic flight bag (EFB) for flight crews as an example.     

面向语义Web服务组合的模糊Petri网推理算法

就语义Web服务自动组合技术而言,在服务组合时需要建立形式化的描述模型,以及有效利用这些已有的数据依赖关系实现服务合成的请求.而现有的组合方法与实施框架可能出现全局状态的指数性增长.针对这一问题,提出一种适合于Web环境的模糊Petri网逆向推理算法.该算法结合模糊Petri网的逆向推理和代数运算的优点,能有效减小算法空间复杂度,把一个大的、复杂的系统转化为一个只与问题相关的、小的系统来处理.最后,通过一个例子说明了算法的可用性.     

Development and implementation of clinical guidelines: An artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.     

基于语义的Web服务发现的设计与实现

目前Web服务发现机制仅提供了简单的基于关键字的搜索方法，缺乏对语义推理的支持。因此提出了一种基于语义的Web服务发现的系统架构，并进行了实现。通过把Web服务的语义描述发布到UDDI注册中心，在服务查找时对服务功能进行语义相似匹配，实现了高效的服务发现。     

面向语义Web语义表示的模糊描述逻辑

分析了语义Web语义表示理论的研究现状及存在的问题,提出了一种新的面向语义Web语义表示的模糊描述逻辑FSHOIQ(fuzzy SHOIQ).给出了FSHOIQ的语法和语义,提出了FSHOIQ的模糊Tableaux的概念,给出了一种基于模糊Tableaux的FSHOIQ的ABox约束下的可满足性推理算法,证明了可满足性推理算法的正确性.提出了FSHOIQ的TBox扩展和去除方法,并证明了FSHOIQ的TBox约束下的包含推理问题可以转化为ABox约束下的可满足性推理问题.FSHOIQ为语义Web表示和推理模糊知识提供了理论基础.