Distributed fuzzy case based reasoning

This paper presents a framework for a distributed knowledge based system by integrating case based reasoning (CBR) and Fuzzy Logic. Fuzzy Logic gives CBR the power to deal with impreciseness and uncertainty. The framework for handling distributed case bases enables our system to construct solution based on collective experience distributed by discipline, time, and geography. In the proposed system the cases can be expressed in terms of attributes that can be crisp as well as fuzzy and appropriately similarity scores are computed. The cases can have attributes from a vocabulary, which can be defined with the constraint of global commitments so that the attributes can be shared and interpreted in a distributed setting. We have implemented a knowledge sharing protocol with common ontology as the repository of exchange vocabulary for knowledge sources with different Universe of Discourses (UOD). We have developed a shell for tailored application development in different domains. We have used RDBMS as the back end repository for cases, DAML + OIL for Ontology design, SAX and DOM for ontology access and RMI for remote procedure call. We have illustrated effectiveness of our approach by developing a travel planning and a help desk application.     

使用约束条件支持领域本体的重用

在利用领域本体知识库(DOKB)中已有领域本体构造新的领域模型或领域本体后，知识工程师需要检查新领域本体以确保其符合规则前提。使用约束务件可以令计算机自动解决这个问题，从而减少知识工程师的工作量,并加快知识的重用过程。     

一种履带式本体构建模型

为了有效地指导本体构建,创建了履带式本体构造模型来描述本体的构造方法。定义和描述了构成此模型的基本要素,包括本体（上层本体、领域本体和应用本体）和本体构造步骤（分析、设计、开发、部署和评估）,并在此基础上做出了定性分析,说明了顶级本体驱动开发方法、领域本体驱动开发方法和应用领域开发方法各自的特点。通过对此模型的分析和应用,以指导在本体开发中使用正确的开发方法。     

基于本体的教学领域知识库建模研究

本体是一种非常有效的知识建模方法.以《自动控制原理》学科为例介绍了本体在构建教学领域知识库模型中的应用,并且用具有很强网络交互性的XML语言实现了该本体的描述.提出用7元属性来描述教学知识概念,并且详细叙述了每个属性的含义及其形式化的定义.这样得到的教学领域知识库本体模型将是一个语义完整、复杂但又清晰的语义网结构.     

OWL-FC: an upper ontology for semantic modeling of Fuzzy Control

This work introduces an OWL-based upper ontology, called OWL-FC (Ontology Web Language for Fuzzy Control), capable to support a semantic definition of Fuzzy Control. It focuses on the fuzzy rules representation by providing domain independent ontology, supporting interoperability and favoring domain ontologies re-usability. The main contribution is that OWL-FC exploits Fuzzy Logic in OWL to model vagueness and uncertainty of the real world. Moreover, OWL-FC enables automatic discovery and execution of fuzzy controllers, by means of context aware parameter setting: appropriate controllers can be activated, depending on the parameters proactively identified in the work environment. In fact, the semantic modeling of concepts allows the characterization of constraints and restrictions for the identification of the right matches between concepts and individuals. OWL-FC ontology provides a wide, semantic-based interoperability among different domain ontologies, through the specification of fuzzy concepts, independently by the application domain. Then, OWL-FC is coherent to the Semantic Web infrastructure and avoids inconsistencies in the ontology.     

基于履带式本体构建方法的多代理模型研究

本体工程为本体构建提供与其相适应的本体开发方法。履带式本体构建方法具有较强的适应性,能够针对本体的不同规模来指导本体开发操作,并对开发过程进行评估。为提高本体构建效率,利用多代理的分布性、自治性、协作性等特点,构建了支持履带式本体开发方法的多代理模型,对其中的代理、通信和代理之间的协作进行了形式化定义和描述,并实现了代理操作、协作等相关算法和代理运行平台。     

应急物流组织本体模型研究及应用

为解决应急物流组织的知识共享及互操作问题,基于ABC上位本体,定义了应急物流本体模型ELO_Ontology,形式化表示为应急物流组织概念、关系、函数、公理和实例的五元组。最后,以四川省汶川大地震中的应急物流组织片段为实例进行了知识表示,结果表明,ELO_Ontology模型能够有效构建应急物流组织的知识体系。     

IT2FS-based ontology with soft-computing mechanism for malware behavior analysis

Antimalware application is one of the most important research issues in the area of cyber security threat. Nowadays, because hackers continuously develop novel techniques to intrude into computer systems for various reasons, many security researchers should analyze and track new malicious program to protect sensitive and valuable information in the organization. In this paper, we propose a novel soft-computing mechanism based on the ontology model for malware behavioral analysis: Malware Analysis Network in Taiwan (MAN in Taiwan, MiT). The core techniques of MiT contain two parts listed as follows: (1) collect the logs of network connection, registry, and memory from the operation system on the physical-virtual hybrid analysis environment to get and extract more unknown malicious behavior information. The important information is then extracted to construct the ontology model by using the Web Ontology Language and Fuzzy Markup Language. Additionally, MiT is also able to automatically provide and share samples and reports via the cloud storage mechanism; (2) apply the techniques of Interval Type-2 Fuzzy Set to construct the malware analysis domain knowledge, namely the Interval Type-2 Fuzzy Malware Ontology (IT2FMO), for malware behavior analysis. Simulation results show that the proposed approach can effectively execute the malware behavior analysis, and the constructed system has also released under GNU General Public License version 3. In the future, the system is expected to largely collect and analyze malware samples for providing industries or universities to do related applications via the established IT2FMO.     

基于本体的机械产品知识仓库研究与应用

为解决产品开发全生命周期中知识管理与知识重用问题,提出了基于本体的机械产品知识仓库系统构架。从语法、属性、结构、实例四个方面进行相似性的计算,实现将源本体的实例映射到目标本体的过程。采用OWL本体表示语言对产品知识进行本体建模。并对基于本体的案例推理进行了深入的研究。最后将研究成果应用于制造业知识管理系统的机械产品知识仓库子系统中。     

Ontology learning: state of the art and open issues

Ontology is one of the fundamental cornerstones of the semantic Web. The pervasive use of ontologies in information sharing and knowledge management calls for efficient and effective approaches to ontology development. Ontology learning, which seeks to discover ontological knowledge from various forms of data automatically or semi-automatically, can overcome the bottleneck of ontology acquisition in ontology development. Despite the significant progress in ontology learning research over the past decade, there remain a number of open problems in this field. This paper provides a comprehensive review and discussion of major issues, challenges, and opportunities in ontology learning. We propose a new learning-oriented model for ontology development and a framework for ontology learning. Moreover, we identify and discuss important dimensions for classifying ontology learning approaches and techniques. In light of the impact of domain on choosing ontology learning approaches, we summarize domain characteristics that can facilitate future ontology learning effort. The paper offers a road map and a variety of insights about this fast-growing field.