BiOSS: A system for biomedical ontology selection

In biomedical informatics, ontologies are considered a key technology for annotating, retrieving and sharing the huge volume of publicly available data. Due to the increasing amount, complexity and variety of existing biomedical ontologies, choosing the ones to be used in a semantic annotation problem or to design a specific application is a difficult task. As a consequence, the design of approaches and tools addressed to facilitate the selection of biomedical ontologies is becoming a priority. In this paper we present BiOSS, a novel system for the selection of biomedical ontologies. BiOSS evaluates the adequacy of an ontology to a given domain according to three different criteria: (1) the extent to which the ontology covers the domain; (2) the semantic richness of the ontology in the domain; (3) the popularity of the ontology in the biomedical community. BiOSS has been applied to 5 representative problems of ontology selection. It also has been compared to existing methods and tools. Results are promising and show the usefulness of BiOSS to solve real-world ontology selection problems. BiOSS is openly available both as a web tool and a web service.     

Ontology-based similarity for product information retrieval

Product development of today is becoming increasingly knowledge intensive. Specifically, design teams face considerable challenges in making effective use of increasing amounts of information. In order to support product information retrieval and reuse, one approach is to use case-based reasoning (CBR) in which problems are solved “by using or adapting solutions to old problems.” In CBR, a case includes both a representation of the problem and a solution to that problem. Case-based reasoning uses similarity measures to identify cases which are more relevant to the problem to be solved. However, most non-numeric similarity measures are based on syntactic grounds, which often fail to produce good matches when confronted with the meaning associated to the words they compare. To overcome this limitation, ontologies can be used to produce similarity measures that are based on semantics. This paper presents an ontology-based approach that can determine the similarity between two classes using feature-based similarity measures that replace features with attributes. The proposed approach is evaluated against other existing similarities. Finally, the effectiveness of the proposed approach is illustrated with a case study on product–service–system design problems.     

Using ontology-based text classification to assist Job Hazard Analysis

The dangers of the construction industry due to the risk of fatal hazards, such as falling from extreme heights, being struck by heavy equipment or materials, and the possibility of electrocution, are well known. The concept of Job Hazard Analysis is commonly used to mitigate and control these occupational hazards. This technique analyzes the major tasks in a construction activity, identifies all potential task-related hazards, and suggests safe approaches to reduce or avoid each of these hazards. In this paper, the authors explore the possibility of leveraging existing construction safety resources to assist JHA, aiming to reduce the level of human effort required. Specifically, the authors apply ontology-based text classification (TC) to match safe approaches identified in existing resources with unsafe scenarios. These safe approaches can serve as initial references and enrich the solution space when performing JHA. Various document modification strategies are applied to existing resources in order to achieve superior TC effectiveness. The end result of this research is a construction safety domain ontology and its underlying knowledge base. A user scenario is also discussed to demonstrate how the ontology supports JHA in practice.     

概念代数在本体合并中的应用

本体合并是本体集成的一种较为有效的方式,是用于解决本体异构以实现本体资源重用和共享的一种方法。引入概念代数以弥补本体的概念深度表达不够、形式化程度低等缺陷。将本体与概念代数结合,提出一种基于概念代数的本体表示方法,其恰好符合领域专家构建本体的意识活动,可以更好地表达和处理知识。概念代数是一种抽象的数学结构,形式化程度较高,并且其概念之间的认知关系更便于进行知识推理。利用概念代数将本体表示为可视化的概念网形式,进而利用概念代数的运算实现概念之间的合并及关联,提出一种基于概念代数的本体合并方法,是对概念代数应用到本体的一种尝试,为本体的处理提供了一个新的视角。     

一种基于同义词词林的中文大规模本体映射方案

本体映射是解决本体异构问题的重要途径和手段,中文知识是网络开放知识库的重要组成部分,但现有的中文本体映射系统在面对大规模本体映射任务时,显得效率较低且可用性不高,目前仍缺乏针对中文大规模本体映射的相关系统。为了解决中文大规模本体的映射问题,设计并实现了一个面向中文的大规模本体映射系统。首先,提出了一种基于拟核力场势函数的大规模本体压缩方法;其次,提出了一种基于同义词词林的中文概念等价关系确定算法;再次,实现了大规模中文本体映射的原型系统;最后,将本系统与相似度计算相关典型算法进行比较,证明其具备一定的可用性和较高的总体性能。     

基于Web服务的Agent模型研究

提出了兼容FIPA Agent的基于Web服务的WS-Agent模型.该模型将Agent分为通讯层、中间消息层和本体层.通讯层为双通道结构,一个用于ACL通讯,另一个用于Web服务通讯.中间消息层将ACL或SOAP消息格式转换为本体处理的统一消息格式.本体层是Agent的逻辑单元.UDDI-DF是兼容Agent DF和Web服务UDDI的目录服务模型,它以Agent DF为中心,利用服务描述翻译器和UDDI代理实现Web服务描述的操作.最后给出了WS-A-gent的通讯机制.     

Applying gravitational search algorithm in the QoS-based Web service selection problem

With the growing use of service-oriented architecture for designing next generation software systems,the service composition problem and its execution complexity have become even more important in resp...