Methodology and intermediate layer for the automatic creation of ontology instances stored in relational databases

In this paper, we introduce our solution for mapping local ontologies to relational and object‐oriented representations. This solution is part of the GeoNis framework for the interoperability of geo‐information systems applications in a local community environment. The GeoNis framework is based on a hybrid ontology approach for data integration. Therefore, a very important subject in our research on semantic data integration is the creation of mapping between a spatial information source and its local ontology. We developed the OWL2RDB mapping language to create an intermediate layer between a relational database and the OWL ontology. This intermediate layer contains rules (expressed in the OWL2RDB language) for mapping between the structural elements of a relational database and the concepts of OWL ontologies. We also present a system that uses the OWL2RDB intermediate layer to create classes that can handle ontology instances stored in relational databases. We have developed a prototype for a tool that uses this proposed approach for the automatic generation of translator/wrapper components in the GeoNis interoperability environment. Copyright © 2012 John Wiley & Sons, Ltd.     

基于领域本体的可信服务组合

针对现有Web服务组合方法缺乏对服务自身属性信任问题的深入考虑,服务组合过程可信需求无法得到保障,提出了一种基于领域本体的可信Web服务组合方法。将服务的非功能属性分为服务质量、服务信任和服务上下文三个主要方面,并采用本体构建工具Protégé构建原子服务本体,利用推理机FaCT++对原始服务集进行本体推理过滤;引入了融合本体推理过滤的Web服务架构;提出了基于层次任务网络(hierarchical tasknetwork,HTN)规划法的可信服务组合算法。仿真实验表明:与传统的组合方法相比,该方法可有效提高服务组合的效率和成功率。     

User‐centric trust and reputation model for personal and trusted service selection

Improving quality of services (QoS) through applying trust and reputation management technology is increasingly popular in the literature and industry. Most existing trust and reputation systems calculate a general trust value or vector based on the gathered feedback without regard to trust's locality and subjectivity; therefore, they cannot effectively support a personal selection with consumer preferences. Our goal is to build a trust and reputation mechanism for facilitating a trustworthy and personal service selection in a service‐oriented Web, where service peers can act as a service provider and/or a service consumer. A user‐centric trust and reputation mechanism distinguishing the different trust context and content to enable a personal service selection with regard to trust preference in a service‐oriented Web is represented in detail. It is widely recognized that reputation‐based trust methods must face the challenge of malicious behaviors. To deal with the malicious feedback behaviors, we introduce a “bidirectional'' feedback mechanism based on QoS experience similarity in our trust and reputation framework. The test run demonstrates that our method can significantly increase the success rate of service transactions and is effective in resisting various malicious behaviors of service peers, when it is compared to other similar methods. © 2011 Wiley Periodicals, Inc.     

Using provenance to debug changing ontologies

On the Semantic Web ontologies evolve and are managed in a distributed setting, e.g. in biomedical databases. Changes are contributed by multiple persons or organizations at various points in time. Often, changes differ by certainty or trustworthiness. When judging changes of automatically inferred knowledge and when debugging such evolving ontologies, the provenance of axioms (e.g. agent, trust degree and modification time) needs to be taken into account. Providing and reasoning with rich provenance data for expressive ontology languages, however, is a non-trivial task.In this paper we propose a formalization of provenance, which allows for the computation of provenance for inferences and inconsistencies. It allows us to answer questions such as “When has this inconsistency been introduced and who is responsible for this change?” as well as “Can I trust this inference?”.We propose a black box algorithm for reasoning with provenance, which is based on general pinpointing, and an optimization, which enables the use of provenance for debugging in real time even for very large and expressive ontologies, such as used in biomedical portals.     

DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO)

Increased availability of mobile computing, such as personal digital assistants (PDAs), creates the potential for constant and intelligent access to up-to-date, integrated and detailed information from the Web, regardless of one's actual geographical position. Intelligent question-answering requires the representation of knowledge from various domains, such as the navigational and discourse context of the user, potential user questions, the information provided by Web services and so on, for example in the form of ontologies. Within the context of the SmartWeb project, we have developed a number of domain-specific ontologies that are relevant for mobile and intelligent user interfaces to open-domain question-answering and information services on the Web. To integrate the various domain-specific ontologies, we have developed a foundational ontology, the SmartSUMO ontology, on the basis of the DOLCE and SUMO ontologies. This allows us to combine all the developed ontologies into a single SmartWeb Integrated Ontology (SWIntO) having a common modeling basis with conceptual clarity and the provision of ontology design patterns for modeling consistency. In this paper, we present SWIntO, describe the design choices we made in its construction, illustrate the use of the ontology through a number of applications, and discuss some of the lessons learned from our experiences.     

Trustworthy assurance of service interoperation in cloud environment

Cloud computing can be realized by service interoperation and its essence is to provide cloud services through network. The development of effective methods to assure the trustworthiness of service interoperation in cloud environment is a very important problem. The essence of cloud security is trust and trust management. Combining quality of service (QoS) with trust model, this paper constructs a QoS-aware and quantitative trust-model that consists of initial trust value, direct trust value, and recommendatory trust value of service, making the provision, discovery, and aggregation of cloud services trustworthy. Hence, it can assure trustworthiness of service interoperation between users and services or among services in cloud environment. At the same time, based on this model, service discovery method based on QoS-aware and quantitative trust-model (TQoS-WSD) is proposed, which makes a solid trust relationship among service requestor, service provider and service recommender, and users can find trustworthy service whose total evaluation value is higher. Compared to QoS-based service discovery (QoS-WSD) method, it is proved by the experiment for TQoS-WSD method that more accurate result of service discovery will be achieved by service requestor, while reasonable time cost is increased. Meanwhile, TQoS-WSD method strongly resists the effect of service discovery by untrustworthy QoS values and improves service invocation success-rate and thus assures trustworthiness of services interoperation.     

Protecting buying agents in e-marketplaces by direct experience trust modelling

In this paper, we describe a framework for modelling the trustworthiness of sellers in the context of an electronic marketplace where multiple selling agents may offer the same good with different qualities and selling agents may alter the quality of their goods. We consider that there may be dishonest sellers in the market (for example, agents who offer goods with high quality and later offer the same goods with very low quality). In our approach, buying agents use a combination of reinforcement learning and trust modelling to enhance their knowledge about selling agents and hence their opportunities to purchase high value goods in the marketplace. This paper focuses on presenting the theoretical results demonstrating how the modelling of trust can protect buying agents from dishonest selling agents. The results show that our proposed buying agents will not be harmed infinitely by dishonest selling agents and therefore will not incur infinite loss, if they are cautious in setting their penalty factor. We also discuss the value of our particular model for trust, in contrast with related work and conclude with directions for future research.     

基于QoS的可信Web服务组合研究

如何合理、高效地选择和组合众多功能相似的服务,为用户提供更优质的服务,是当前SOA领域研究的重点和难点。服务质量QoS(Quafity of Service)的引入,在一定程度上保证了服务组合的合理性,但对于如何确保服务组合的可信,目前还没有一个比较完善和可行的方案。因此,提出了可信Web服务组合模型(Trust Web Service Composition Modcl,TWSCM),并通过在原有Wcb服务体系架构上增加QoS代理和可信模块来确保服务组合的质量和可信。最后,在语言网格上进行了相关实验,结果表明,该方法能为用户提供高质量、可信的组合服务,从而验证了该方法的合理性和有效性。     

Experimentation with local consensus ontologies with implications for automated service composition

Agent technologies represent a promising approach for the integration of interorganizational capabilities across distributed, networked environments. However, knowledge sharing interoperability problems can arise when agents incorporating differing ontologies try to synchronize their internal information. Moreover, in practice, agents may not have a common or global consensus ontology that will facilitate knowledge sharing and integration of functional capabilities. We propose a method to enable agents to develop a local consensus ontology during operation time as needed. By identifying similarities in the ontologies of their peer agents, a set of agents can discover new concepts/relations and integrate them into a local consensus ontology on demand. We evaluate this method, both syntactically and semantically, when forming local consensus ontologies with and without the use of a lexical database. We also report on the effects when several factors, such as the similarity measure, the relation search level depth, and the merge order, are varied. Finally, experimenting in the domain of agent-supported Web service composition, we demonstrate how our method allows us to successfully autonomously form service-oriented local consensus ontologies.     

Trust‐annotated ontology integration using social modelling*

Abstract: As ontologies become more prevalent for information management the need to manage the ontologies increases. Multiple organizations, within a domain, often combine to work on specific projects. When separate organizations come together to communicate, an alignment of terminology and semantics is required. Ontology creation is often privatized for these individual organizations to represent their view of the domain. This creates problems with alignment and integration, making it necessary to consider how much each ontology should influence the current decision to be made. To assist with determining influence a trust‐based approach on authors and their ontologies provides a mechanism for ranking reasoning results. A representation of authors and the individual resources they provide for the merged ontology becomes necessary. The authors are then weighted by trust and trust for the resources they provide the ontology is calculated. This is then used to assist the integration process allowing for an evolutionary trust model to calculate the level of credibility of resources. Once the integration is complete semantic agreement between ontologies allows for the revision of the authors' trust.     

一种双层P2P结构的语义服务发现模型

在开放的Internet环境下,多本体共存是一种必然.同时,集中式的语义服务发现机制是整个面向服务计算的瓶颈,导致系统的可扩展性差.为了支持多本体共存并提高系统的可扩展性,提出一种双层P2P语义服务发现模型.该模型以本体社区为核心,将iVCE(Internet-based virtual computing environment)的核心概念集成到P2P模型中.基于该模型,提出一种分两个阶段、3个步骤的服务发现算法.除了在本体社区内以外,算法还可以实现跨社区的服务发现.在本体社区内,算法首先根据语义相似性选定相应的注册服务器,然后再利用逻辑推理来实施精确的服务匹配.而跨社区的服务发现则按照一定的策略实施.该模型适用于多本体共存的开放环境.实验结果表明,通过合理的参数配置,模型能在查全率和服务发现响应时间之间加以折衷,并取得较好的结果;另外,模型能够在不降低服务查全率的情况下降低注册服务节点的平均负载.     

一种跨本体的语义相似度计算方法

针对在广域分布环境下进行信息共享与服务的需要,本文设计了基于本体的元数据模型,并在MD3模型的基础上给出了一种基于该元数据模型的跨本体的语义相似度计算方法.MD3模型是一种系统的跨本体概念间相似度的计算方法,这种方法无需建立一个集成的共享本体.在MD3模型的基础上,充分利用本体对概念的描述信息,重点讨论了跨本体概念间非层次关系相似度的计算,把MD3模型扩展到MD4模型,使得概念间相似度的计算理论上更全面、更精确.     

一种面向语义网服务的本体映射框架*

本体的异构性阻碍了语义网服务的互操作。从解决语义网服务中本体的异构问题出发,同时考虑到目前的本体映射系统大多效率不高、映射结果不够准确的问题,提出了一种适用于语义网服务的本体映射方法及系统框架。该方法利用机器学习技术来提高本体映射的自动化程度,利用综合评判技术修正映射结果,以提高本体映射的准确率。采用OAEI 2007的基准测试数据集benchmarks进行实验测试,结果表明本系统的性能基本达到预期效果,能够有效地解决语义网服务中的本体异构问题。     

MD4：一种综合的跨本体实体语义相似度计算方法*

面向广域分布环境下信息资源共享与服务的需要,设计了基于本体的元数据模型,并在MD3模型的基础上给出了一种基于该元数据模型的跨本体的语义相似度计算方法——MD4模型。MD4充分利用本体对实体的描述信息,重点讨论了实体名称、实体属性、实体语义环境以及实体实例等相似度的计算,把MD3模型扩展到MD4模型,使得信息资源实体间语义相似度的计算更全面、精确。