FaBiO and CiTO: Ontologies for describing bibliographic resources and citations

Semantic publishing is the use of Web and Semantic Web technologies to enhance the meaning of a published journal article, to facilitate its automated discovery, to enable its linking to semantically related articles, to provide access to data within the article in actionable form, and to facilitate integration of data between articles. Recently, semantic publishing has opened the possibility of a major step forward in the digital publishing world. For this to succeed, new semantic models and visualization tools are required to fully meet the specific needs of authors and publishers. In this article, we introduce the principles and architectures of two new ontologies central to the task of semantic publishing: FaBiO, the FRBR-aligned Bibliographic Ontology, an ontology for recording and publishing bibliographic records of scholarly endeavours on the Semantic Web, and CiTO, the Citation Typing Ontology, an ontology for the characterization of bibliographic citations both factually and rhetorically. We present those two models step by step, in order to emphasise their features and to stress their advantages relative to other pre-existing information models. Finally, we review the uptake of FaBiO and CiTO within the academic and publishing communities.     

基于语义的访问控制模型及其推理机制

对语义Web上资源进行访问要求授权决策充分考虑实体之间的语义关系,但传统的访问控制模型不能处理该问题。结合基于本体的语义描述技术和基于语义规则的推理机制,将不同的语义内部关系归纳为包含关系,提出一种面向语义Web的基于语义的访问控制模型,研究其语义授权推理机制,并提出一个推理实现系统。     

本体存储技术研究

Ontology是对一个特定领域中重要概念的共享的形式化的描述,由于具有明确性和共享性,它可以作为领域内不同主体之间进行交流的语义基础;更进一步的,Ontology可以帮助机器理解文档表达的语义信息.语义网络是Ontology的一个重要应用场景,Ontology用来描述网络资源的语义,从而使机器具有自动管理网络信息的能力.那么巨大的数据规模是语义网络环境下Ontology数据存储管理面临的一个突出问题,所以介绍了本体存储的方法、存储模式及几种典型的本体存储管理系统.讨论了当前本体存储模式的问题并展望了未来的发展方向.     

本体存储技术研究

Ontobgy是对一个特定领域中重要概念的共享的形式化的描述,由于具有明确性和共享性,它可以作为领域内不同主体之同进行交流的语义基础;更进一步的,Ontology可以帮助机器理解文档表达的语义信息。语义网络是Ontology的一个重要应用场景,Ontolcgy用来描述网络资源的语义,从而使机器具有自动管理网络信息的能力。那么巨大的数据规模是语义网络环境下Ontology数据存储管理面临的一个突出问题,所以介绍了本体存储的方法、存储模式及几种典型的本体存储管理系统。讨论了当前本体存储模式的问题并展望了未来的发展方向。     

Where to publish and find ontologies? A survey of ontology libraries

One of the key promises of the Semantic Web is its potential to enable and facilitate data interoperability. The ability of data providers and application developers to share and reuse ontologies is a critical component of this data interoperability: if different applications and data sources use the same set of well defined terms for describing their domain and data, it will be much easier for them to “talk” to one another. Ontology libraries are the systems that collect ontologies from different sources and facilitate the tasks of finding, exploring, and using these ontologies. Thus ontology libraries can serve as a link in enabling diverse users and applications to discover, evaluate, use, and publish ontologies. In this paper, we provide a survey of the growing—and surprisingly diverse—landscape of ontology libraries. We highlight how the varying scope and intended use of the libraries affects their features, content, and potential exploitation in applications. From reviewing 11 ontology libraries, we identify a core set of questions that ontology practitioners and users should consider in choosing an ontology library for finding ontologies or publishing their own. We also discuss the research challenges that emerge from this survey, for the developers of ontology libraries to address.     

语义集成中的不确定性本体映射模型

语义Web的高速发展使其具有动态性和异构性特征,解决语义信息的异构性问题成为实现信息集成的关键。本体作为一种语义Web的知识表示形式,增强了Web的语义信息。因此,为了解决语义异构性,实现数据间的互操作,必须建立异构本体间的映射关系。然而,为庞大的异构本体建立完全精确的本体映射是不现实的,本体映射中存在一定的不确定性。提出了一种新型的本体映射框架——语义集成中的不确定性本体映射。从不同方面研究本体特征,集合了多种映射策略,并引入了各映射策略中不确定性匹配的解决方案。实验证明,该方法具有可靠的实验性能,并且具有很好的通用性和可扩展性。     

Semantic web reasoners and languages

Semantic web reasoners and languages enable the semantic web to function. Some of the latest reasoning models developed in the last few years are: DLP, FaCT, RACER, Pellet, MSPASS, CEL, Cerebra Engine, QuOnto, KAON2, HermiT and others. Some software tools such as Protégé, Jena and others also have been developed, which provide inferencing as well as ontology development and management environments. These reasoners usually differ in their inference procedures, supporting logic, completeness of reasoning, expressiveness and implementation languages. Various semantic web languages with increasing expressive power continue to be developed for describing web services. We survey the some of the more recent languages like OWL-S (Ontology Web Language-Schema), WSML (Web Service Modeling Language), SWRL (Semantic Web Rule Language) and others that have been tested in early use. We also survey semantic web reasoners and their relationship to these languages.     

一种本体知识文摘方法的研究和实现

本体作为领域知识的表示方法，已经成为语义Web的基础。本体通常由领域专家建立，用于表示领域中概念以及概念与概念之间的关系。但这也使得普通用户难以理解本体中描述的信息。普通用户往往希望本体中的信息能够以自然语言的形式描述。这正是本文讨论的主要问题。本文采用分治策略，利用基于嵌套复杂模板的解决方案，设计并实现了本体知识文摘的算法。我们开发了一个原型系统SWARMS，并将该文摘算法进行了运用。初步的实验表明，本文提出的方法取得较好的结果。     

The Semantic Web: the roles of XML and RDF

XML and RDF are the current standards for establishing semantic interoperability on the Web, but XML addresses only document structure. RDF better facilitates interoperation because it provides a data model that can be extended to address sophisticated ontology representation techniques. We explain the role of ontologies in the architecture of the Semantic Web. We then briefly summarize key elements of XML and RDF, showing why using XML as a tool for semantic interoperability will be ineffective in the long run. We argue that a further representation and inference layer is needed on top of the Web's current layers, and to establish such a layer, we propose a general method for encoding ontology representation languages into RDF/RDF schema. We illustrate the extension method by applying it to Ontology Interchange Language, an ontology representation and inference language     

面向资源融合网格的数据语义框架

当前语义web服务成了语义web研究和应用的热点之一,提出了几种语义web服务框架,但是这些架构主要是用于web服务的自动发现、自动调用、自动服务纽合,不适合描述网格环境下的数据语义。本文在目前的语义web服务框架基础上,提出面向资源容融合网格（FuseGrid）的数据语义框架（ODSF）。在ODSF中,数据对象的语义是自描述的;ODSF包括4个顶层元素,分别为元属性、数据库本体、映射以及业务规则,其中元属性、数据库本体以及映射本体表示数据对象的静态语义,而业务规则描述数据对象的动态语义。同时分析了该框架的语义数据访问方法及其关键算法。     

一种关系模式到OWL DL本体的翻译方法

本体技术是数据可以达到语义层次交换的关键,如何将当前各类数据形成本体知识库表示是一个非常重要的问题。针对这个问题,以一种关系模式到一种语义扩展ER模型的正确性可满足转换算法为基础,提出了一种通过数据库反向工程到OWL DL本体的翻译算法,说明了该算法使得转换是正确性可满足的,并实验实现验证了算法。     

基于UDDI的语义Web服务门户的设计与实现

统一描述发现集成协议（UDDI）提供了Web服务的信息注册查找规范,解决了Web服务的描述、发布以及查找问题。然而采用UDDI建立的Web服务在服务查找时会受关键词匹配的严重影响,缺乏对语义信息的支持。因此在服务匹配时不能搜索到用不同术语描述的相关信息,从而造成了服务查全率查准率低。语义Web服务技术以信息服务智能化、自动化、高效性为目标,通过本体（Ontology）来描述资源的语义信息,它能够有效的解决上述问题。本文在分析了语义Web服务和UDDI技术规范的基础上。设计实现了一种基于UDDI的语义WEB服务的发布及发现机制。     

Learning the semantics of structured data sources

Information sources such as relational databases, spreadsheets, XML, JSON, and Web APIs contain a tremendous amount of structured data that can be leveraged to build and augment knowledge graphs. However, they rarely provide a semantic model to describe their contents. Semantic models of data sources represent the implicit meaning of the data by specifying the concepts and the relationships within the data. Such models are the key ingredients to automatically publish the data into knowledge graphs. Manually modeling the semantics of data sources requires significant effort and expertise, and although desirable, building these models automatically is a challenging problem. Most of the related work focuses on semantic annotation of the data fields (source attributes). However, constructing a semantic model that explicitly describes the relationships between the attributes in addition to their semantic types is critical.We present a novel approach that exploits the knowledge from a domain ontology and the semantic models of previously modeled sources to automatically learn a rich semantic model for a new source. This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology. Given some sample data from the new source, we leverage the knowledge in the domain ontology and the known semantic models to construct a weighted graph that represents the space of plausible semantic models for the new source. Then, we compute the top k candidate semantic models and suggest to the user a ranked list of the semantic models for the new source. The approach takes into account user corrections to learn more accurate semantic models on future data sources. Our evaluation shows that our method generates expressive semantic models for data sources and services with minimal user input. These precise models make it possible to automatically integrate the data across sources and provide rich support for source discovery and service composition. They also make it possible to automatically publish semantic data into knowledge graphs.     

语义Web与本体研究综述

语义Web是一个新兴的研究方向，Ontology在语义Web中的本体应用研究还在初级阶段。介绍了Ontology的定义和描述语言，建设方法和工具，以及主要研究机构，并介绍了目前语义Web中Ontology的研究和成果。     

基于本体论的语义建模研究

本体(Ontology)是下一代互联网(SemanticWeb)的基础,OWL语言是W3C组织定义的本体描述语言。鉴于当前互联网的规模越来越庞大,如何准确快速地获取信息正变得至关重要,而基于本体论的语义模型为信息的表示、交换和处理提供了一个较为合理的标准,从而使得网上信息的完全共享成为可能。文中阐述了本体的概念,重点探讨了基于本体论的语义建模方法和OWL语言对本体表示的支持,并且具体给出了一个基于OWL语言的建模实例。     

Toward a New Generation of Semantic Web Applications

Although research on integrating semantics with the Web started almost as soon as the Web was in place, a concrete Semantic Web that is, a large-scale collection of distributed semantic metadata emerged only over the past four to five years. The Semantic Web's embryonic nature is reflected in its existing applications. Most of these applications tend to produce and consume their own data, much like traditional knowledge- based applications, rather than actually exploiting the Semantic Web as a large-scale information source. These first-generation semantic Web applications typically use a single ontology that supports integration of resources selected at design time.     

WODOS:一个语义Web支撑软件的研究与实现

随着语义Web相关标准的日益完善，对其支撑软件的研究也逐渐深入．分析了目前语义Web的支撑软件研究现状，并对相关研究项目进行比较．在自身研究工作的基础上，设计并实现了一个面向语义Web的分布式本体系统的支撑软件——WODOS(Web Oriented Distributed Ontology System)．该支撑软件在保证本体所必需的唯一性与一致性的前提下．能够对本体进行分布存储、引用、处理，并在此基础上实现基于本体的推理机制．     

Clustering rule bases using ontology-based similarity measures

Rules are increasingly becoming an important form of knowledge representation on the Semantic Web. There are currently few methods that can ensure that the acquisition and management of rules can scale to the size of the Web. We previously developed methods to help manage large rule bases using syntactical analyses of rules. This approach did not incorporate semantics. As a result, rule categorization based on syntactic features may not be effective. In this paper, we present a novel approach for grouping rules based on whether the rule elements share relationships within a domain ontology. We have developed our method for rules specified in the Semantic Web Rule Language (SWRL), which is based on the Web Ontology Language (OWL) and shares its formal underpinnings. Our method uses vector space modeling of rule atoms and an ontology-based semantic similarity measure. We apply a clustering method to detect rule relatedness, and we use a statistical model selection method to find the optimal number of clusters within a rule base. Using three different SWRL rule bases, we evaluated the results of our semantic clustering method against those of our syntactic approach. We have found that our new approach creates clusters that better match the rule bases’ logical structures. Semantic clustering of rule bases may help users to more rapidly comprehend, acquire, and manage the growing numbers of rules on the Semantic Web.     

Semantic Web search based on ontological conjunctive queries

Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present a novel approach to Semantic Web search, called Serene, which allows for a semantic processing of Web search queries, and for evaluating complex Web search queries that involve reasoning over the Web. More specifically, we first add ontological structure and semantics to Web pages, which then allows for both attaching a meaning to Web search queries and Web pages, and for formulating and processing ontology-based complex Web search queries (i.e., conjunctive queries) that involve reasoning over the Web. Here, we assume the existence of an underlying ontology (in a lightweight ontology language) relative to which Web pages are annotated and Web search queries are formulated. Depending on whether we use a general or a specialized ontology, we thus obtain a general or a vertical Semantic Web search interface, respectively. That is, we are actually mapping the Web into an ontological knowledge base, which then allows for Semantic Web search relative to the underlying ontology. The latter is then realized by reduction to standard Web search on standard Web pages and logically completed ontological annotations. That is, standard Web search engines are used as the main inference motor for ontology-based Semantic Web search. We develop the formal model behind this approach and also provide an implementation in desktop search. Furthermore, we report on extensive experiments, including an implemented Semantic Web search on the Internet Movie Database.     

从ER模式到OWL DL本体的语义保持的翻译

提出了一种从ER模式到OWL DL本体的语义保持的翻译方法.该方法在形式化表示ER模式的基础上,建立ER模式和OWL DL本体之间精确的概念对应,通过一个翻译算法按照一组预定义的映射规则实现模式翻译.理论分析表明,该方法是语义保持的和有效的;算法实现和案例研究进一步证实,完全自动的机器翻译是可实现的.该文方法是原创性的,为Web本体的开发以及数据库和语义Web之间语义互操作的实现开辟了一条有效途径.