Supply chain management ontology from an ontology engineering perspective

Knowledge sharing and reuse are important factors affecting the performance of supply chains. These factors can be amplified in information systems by supply chain management (SCM) ontology. The literature provides various SCM ontologies for a range of industries and tasks. Although many studies make claims of the benefits of SCM ontology, it is unclear to what degree the development of these ontologies is informed by research outcomes from the ontology engineering field. This field has produced a set of specific engineering techniques, which are supposed to help developing quality ontologies. This article reports a study that assesses the adoption of ontology engineering techniques in 16 SCM ontologies. Based on these findings, several implications for research as well as SCM ontology adoption are articulated.     

基于Ontology的异构信息集成研究

语义异构问题是当前信息集成领域的研究热点。本文讨论了信息集成的方法及其存在的一些问题,介绍了本体的概念、本体在信息集成中的角色,提出了一种基于本体的信息集成架构,旨在实现对语义异构信息源的集成。     

Evaluating techniques for learning non-taxonomic relationships of ontologies from text

Learning non-taxonomic relationships is a sub-field of Ontology Learning that aims at automating the extraction of these relationships from text. Several techniques have been proposed based on Natural Language Processing and Machine Learning. However just like for other techniques for Ontology Learning, evaluating techniques for learning non-taxonomic relationships is an open problem. Three general proposals suggest that the learned ontologies can be evaluated in an executable application or by domain experts or even by a comparison with a predefined reference ontology. This article proposes two procedures to evaluate techniques for learning non-taxonomic relationships based on the comparison of the relationships obtained with those of a reference ontology. Also, these procedures are used in the evaluation of two state of the art techniques performing the extraction of relationships from two corpora in the domains of biology and Family Law.     

Optimizing ontology alignment through Memetic Algorithm based on Partial Reference Alignment

All the state of the art approaches based on evolutionary algorithm (EA) for addressing the meta-matching problem in ontology alignment require the domain expert to provide a reference alignment (RA) between two ontologies in advance. Since the RA is very expensive to obtain especially when the scale of ontology is very large, in this paper, we propose to use the Partial Reference Alignment (PRA) built by clustering-based approach to take the place of RA in the process of using evolutionary approach. Then a problem-specific Memetic Algorithm (MA) is proposed to address the meta-matching problem by optimizing the aggregation of three different basic similarity measures (Syntactic Measure, Linguistic Measure and Taxonomy based Measure) into a single similarity metric. The experimental results have shown that using PRA constructed by our approach in most cases leads to higher quality of solution than using PRA built in randomly selecting classes from ontology and the quality of solution is very close to the approach using RA where the precision value of solution is generally high. Comparing to the state of the art ontology matching systems, our approach is able to obtain more accurate results. Moreover, our approach’s performance is better than GOAL approach based on Genetic Algorithm (GA) and RA with the average improvement up to 50.61%. Therefore, the proposed approach is both effective.     

Ontology design with a granular approach

Ontology design for complex applications is quite a challenge. The quality of an ontology is highly dependent upon the capabilities of designers, and the collaborative design process is hampered by the difficulty of balancing the viewpoints of different designers. In this paper, we present a granular view of ontology: ontologies are granular, ontologies are granular approximations of conceptualizations and conceptual-relation granules of an ontology are ordered tuples. We then propose a corresponding granular ontology design approach. In our granular ontology design approach, the unified granular cognition level and hierarchies of sub-concepts are initialized before ontological terms are designed in detail, which reduces the subjective effects of the capabilities of designers. Our approach also introduces the idea of optimization to choose an optimal subset, which can best approximate the real concept domain, from the knowledge rule set presented by different domain experts. The optimal subset is chosen on the basis of the principle of granular ontology knowledge structure.     

本体编辑器研究与实现初探

随着语义网研究的发展,本体的地位与作用日显重要,在计算机中实现交互友好、功能完整的本体编辑是其中最基础、最首要的工作。本文对本体编辑器的一般组成及功能结构进行了详细研究,总结出本体构建的"八步法",并提出了一种面向中文用户、能导入导出多种格式的本体文件、实现了一定程度上的推理功能的本体编辑器实现方法。     

基于网络课程本体的语义查询推理系统

针对网络课程知识点相对孤立、共享性与重用性差、学习资源查找困难、不能提供个性化服务等问题,提出使用基于本体查询推理的解决方法,设计了检索、查询和推理的功能模块和工作流程,并综合运用了Jena和SPARQL实现了相应的原型系统。通过具体的课程本体进行测试,验证了该解决方法的有效性和可行性。该系统能够将网络课程知识内容有机组织到一起,达到共享、重用的效果,可以提供个性化的学习服务,为进一步开发智能化的网络课程平台提供了条件。     

基于本体的数据挖掘技术应用于商务智能中的实际应用

首先阐述了挖掘技术与商务智能的含义,指出了数据挖掘技术应用在商务智能中的意义,结合新时期我国各大企业的发展实际,对基于本体的数据挖掘技术应用在商务智能中的实际情况进行了分析,旨在利用数据挖掘技术,发挥出企业商务智能系统的优势,提高企业核心竞争力,促进企业长远发展。     

装备保障仿真概念模型的语义验证方法研究

概念模型验证是保证概念模型正确、可信的重要手段。针对现有概念模型形式化验证方法复杂、繁琐,非形式化验证方法的主观性较强、可信性不高等问题,采用本体理论和语义网技术,提出了一种基于本体与规则推理的装备保障仿真概念模型语义验证方法。该方法的思路是:首先将UML描述的概念模型转化为本体描述语言OWL描述的概念模型;然后根据领域知识构建验证规则,并运用语义网规则描述语言SWRL描述验证规则;最后将模型和规则转换为Jess规则引擎识别的数据格式,输入到Jess规则引擎中进行模型与规则的语义推理,检查概念模型是否符合验证规则。实例表明,该方法使用验证规则和语义推理机替代领域专家在计算机上自动对概念模型的语义内容进行验证,提高了验证效率,减少了专家验证的主观性和不确定性,降低了形式化验证方法的复杂性。     

属性RBAC策略的OWL表示和推理

将属性作为授权约束,给出了属性扩展的RBAC模型。提出了一种基于OWL的属性RBAC策略定义和表示方法。该方法支持复杂属性表达式、属性值偏序关系、角色层次关系和约束的定义;在推理机的支持下,可以执行访问控制决策推理,属性表达式支配关系判定和策略知识一致性检测。具体应用案例说明了该方法的可行性。