一种基于语义的协同工作模型

针对产品制造领域,提出一种基于语义的协同工作模型。该模型分别以XML和RDF(S)作为协同信息的语法描述模式和语义描述模型,为协同信息提供机器呵处理和可理解能力;通过建立由个体Ontology和顶层Ontology构成的两层Ontology体系,以个体Ontology描述协同过程中个人或团体的领域背景知识,以顶层Ontology为概念和语义参照模型,建立协同信息之间的语义约束,为协同过程提供多领域知识共享和互操作环境。     

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     

Extending the Unified Modeling Language for ontology development

There is rapidly growing momentum for web enabled agents that reason about and dynamically integrate the appropriate knowledge and services at run-time. The dynamic integration of knowledge and services depends on the existence of explicit declarative semantic models (ontologies). We have been building tools for ontology development based on the Unified Modeling Language (UML). This allows the many mature UML tools, models and expertise to be applied to knowledge representation systems, not only for visualizing complex ontologies but also for managing the ontology development process. UML has many features, such as profiles, global modularity and extension mechanisms that are not generally available in most ontology languages. However, ontology languages have some features that UML does not support. Our paper identifies the similarities and differences (with examples) between UML and the ontology languages RDF and DAML+OIL. To reconcile these differences, we propose a modification to the UML metamodel to address some of the most problematic differences. One of these is the ontological concept variously called a property, relation or predicate. This notion corresponds to the UML concepts of association and attribute. In ontology languages properties are first-class modeling elements, but UML associations and attributes are not first-class. Our proposal is backward-compatible with existing UML models while enhancing its viability for ontology modeling. While we have focused on RDF and DAML+OIL in our research and development activities, the same issues apply to many of the knowledge representation languages. This is especially the case for semantic network and concept graph approaches to knowledge representations. Initial sbmission: 16 February 2002 / Revised submission: 15 October 2002 Published online: 2 December 2002     

A Rule-Based Object-Oriented OWL Reasoner

In this paper, we describe O-DEVICE, a memory-based knowledge-based system for reasoning and querying OWL ontologies by implementing RDF/OWL entailments in the form of production rules in order to apply the formal semantics of the language. Our approach is based on a transformation procedure of OWL ontologies into an object-oriented schema and the application of inference production rules over the generated objects in order to implement the various semantics of OWL. In order to enhance the performance of the system, we introduce a dynamic approach of generating production rules for ABOX reasoning and an incremental approach of loading ontologies. O-DEVICE is built over the CLIPS production rule system, using the object-oriented language COOL to model and handle ontology concepts and RDF resources. One of the contributions of our work is that we enable a well-known and efficient production rule system to handle OWL ontologies. We argue that although native OWL rule reasoners may process ontology information faster, they lack some of the key features that rule systems offer, such as the efficient manipulation of the information through complex rule programs. We present a comparison of our system with other OWL reasoners, showing that O-DEVICE can constitute a practical rule environment for ontology manipulation.     

Ontology learning for the Semantic Web

The Semantic Web relies heavily on formal ontologies to structure data for comprehensive and transportable machine understanding. Thus, the proliferation of ontologies factors largely in the Semantic Web's success. The authors present an ontology learning framework that extends typical ontology engineering environments by using semiautomatic ontology construction tools. The framework encompasses ontology import, extraction, pruning, refinement and evaluation.     

Interoperability results for Semantic Web technologies using OWL as the interchange language

Using Semantic Web technologies in complex scenarios requires that such technologies correctly interoperate by interchanging ontologies using the RDF(S) and OWL languages. This interoperability is not straightforward because of the high heterogeneity in Semantic Web technologies and, while the number of such technologies grows, affordable mechanisms for evaluating Semantic Web technology interoperability are needed to comprehend the current and future interoperability of Semantic Web technologies.This paper presents the OWL Interoperability Benchmarking, an international benchmarking activity that involved the evaluation of the interoperability of different Semantic Web technologies using OWL as the interchange language. It describes the evaluation resources used in this benchmarking activity, the OWL Lite Import Benchmark Suite and the IBSE tool, and presents how to use them for evaluating the OWL interoperability of Semantic Web technologies. Moreover, the paper offers an overview of the OWL interoperability results of the eight tools participating in the benchmarking: one ontology-based annotation tool (GATE), three ontology frameworks (Jena, KAON2, and SWI-Prolog), and four ontology development tools (Protégé Frames, Protégé OWL, SemTalk, and WebODE).     

Defining assessment projects and scenarios for policy support: Use of ontology in Integrated Assessment and Modelling

Integrated Assessment and Modelling (IAM) provides an interdisciplinary approach to support ex-ante decision-making by combining quantitative models representing different systems and scales into a framework for integrated assessment. Scenarios in IAM are developed in the interaction between scientists and stakeholders to explore possible pathways of future development. As IAM typically combines models from different disciplines, there is a clear need for a consistent definition and implementation of scenarios across models, policy problems and scales. This paper presents such a unified conceptualization for scenario and assessment projects. We demonstrate the use of common ontologies in building this unified conceptualization, e.g. a common ontology on assessment projects and scenarios. The common ontology and the process of ontology engineering are used in a case study, which refers to the development of SEAMLESS-IF, an integrated modelling framework to assess agricultural and environmental policy options as to their contribution to sustainable development. The presented common ontology on assessment projects and scenarios can be reused by IAM consortia and if required, adapted by using the process of ontology engineering as proposed in this paper.     

New perspectives in ontological analysis: Guidelines and rules for incorporating modelling languages into UEML

Ontological analysis of modelling languages has been mainly used for evaluating quality of modelling language w.r.t. one specific upper ontology. Generally speaking this evaluation has been done by identifying the coverage of the modelling language constructs w.r.t. the ontology and vice-versa. However, a quite limited support has been developed for performing the ontological analysis task. Specifically, various ontologies used for ontological analysis are not associated to a machine readable format; the coverage of modelling language constructs is mostly provided by informal tables mapping one construct on to one ontological concept; the way in which this coverage task is undertaken is poorly specified (resulting in distinct results for distinct experts involved), and finally, preventing any ontology enrichment for dealing with some specialised language constructs. This limited support also prevents application of ontological analysis outcomes to problems and domains dealing with interoperability, integration and integrated usage of enterprise and IS models, which is today one of the key aspects for making interoperable, maintainable and evolvable inter and intra enterprise software systems. The paper provides an overview of the Unified Enterprise Modelling Language (UEML) approach, which introduces advanced support to ontological analysis of modelling languages. The paper is specifically focused on the task of ontological analysis of modelling languages (named incorporation of modelling languages) by introducing and explaining several guidelines and rules for driving the task: therefore, not all the aspects of the UEML approach will be discussed through the paper. The guidelines and rules are illustrated by incorporation of three selected modelling constructs from IDEF3, a well known language for specifying enterprise processes.     

Modeling Incomplete Knowledge of Semantic Web Using Bayesian Networks

ABSTRACT

Interoperable ontologies already exist in the biomedical field, enabling scientists to communicate with minimum ambiguity. Unfortunately, ontology languages, in the semantic web, such as OWL and RDF(S), are based on crisp logic and thus they cannot handle uncertain knowledge about an application field, which is unsuitable for the medical domain. In this paper, we focus on modeling incomplete knowledge in the classical OWL ontologies, using Bayesian networks, all keeping the semantic of the first ontology, and applying algorithms dedicated to learn parameters of Bayesian networks in order to generate the Bayesian networks. We use EM algorithm for learning conditional probability tables of different nodes of Bayesian network automatically, contrary to different tools of Bayesian networks where probabilities are inserted manually. To validate our work, we have applied our model on the diagnosis of liver cancer using classical ontology containing incomplete instances, in order to handle medical uncertain knowledge, for predicting a liver cancer.     

CNLs for the semantic web: a state of the art

One of the core challenges for building the semantic web is the creation of ontologies, a process known as ontology authoring. Controlled natural languages (CNLs) propose different frameworks for interfacing and creating ontologies in semantic web systems using restricted natural language. However, in order to engage non-expert users with no background in knowledge engineering, these language interfacing must be reliable, easy to understand and accepted by users. This paper includes the state-of-the-art for CNLs in terms of ontology authoring and the semantic web. In addition, it includes a detailed analysis of user evaluations with respect to each CNL and offers analytic conclusions with respect to the field.     

一种基于RDF图的本体匹配方法

本体匹配是建立两个本体之间映射关系的过程,一个高效、严格的相似度计算方法是本体匹配的前提条件,为此提出了一种基于RDF图匹配的方法。该方法用RDF图表示本体,使本体间的匹配问题转化为RDF图的匹配问题,并利用匹配树表示匹配的状态,通过匹配树计算出两个本体中各实体之间的相似度,进而得到两个本体之间的映射关系。实验结果表明,该方法在查全率和查准率方面都有很好的表现。     

ORM模型转换为OWL 2公理的映射规则

对象角色建模方法ORM目前已发展成为一种本体工程方法。需要将ORM表达的领域知识发布到语义Web上,以供不同应用系统共享和交换。OWL 2是W3C推荐使用的语义Web本体语言。探讨了将ORM模型映射为OWL 2公理的方法,通过模型等价变换和引入新的公理,给出了所有可行的模型映射规则。     

一种基于本体的语义检索算法

本体技术作为一种能在语义和知识层次上描述概念体系的有效工具,在数字图书馆得到了广泛的关注。给出了本体结构及其词法的形式化定义。为解决RDF在语义检索中存在的问题,利用Jena工具,提出了一种提取和处理RDF层本体处理方法,给出了一种基于本体的语义检索算法。算法基于软件工程的思想,忽略不同的本体语言、本体的RDF层集合间的差异。算法分五步骤进行,包括：将RDF层本体信息从网页中分离并构建RDF模型、对RDF模型进行集合运算、RDF层本体的查询、修正RDF层本体以及对RDF层本体的序列化。实验结果表明缩短了查询时间,提高了检索的查全率及查准率。     

Using OWL-VisMod through a decision-making process for reusing OWL ontologies

Knowledge representation (KR) can be defined as a set of ontological commitments, provided with the capabilities of performing inference. The knowledge can be represented using an ontology, which provides a shared insight into a certain domain. The use of ontologies to represent knowledge also allows interoperation among knowledge-based systems. The process of building ontologies can be tedious and sometimes exhaustive. A possible solution in order to avoid this problem would be to reuse the ontologies previously created by others. This paper describes a case study of reusability using OWL-VisMod, a tool designed for developing ontological engineering based on visual conceptual modelling for OWL ontologies. A workflow performed with OWL-VisMod is described; including a decision-making process in order to decide whether or not it could be desirable to reuse an ontology, according to the requirements of a certain project.     

JOINT: Java ontology integrated toolkit

In the past few years, the use of ontologies for creating more intelligent and effective application has increased considerably. This growth is due to the fact that ontologies attempt to provide semantics to the data consumed by machines so that they can reason about this data. However, developing complex ontology-based applications is still difficult and time-consuming because the existing tools do not provide a simple and unified environment for developers. Most of these tools only provide data manipulation using RDF triples, complicating the development of applications that need to work with the object orientation paradigm. Furthermore, tools that provide instances manipulation via object orientation do not support features such as manipulating ontologies, reasoning over rules or querying data with SPARQL. In this context, this work proposes a framework and a tool for supporting the efficient development of ontology-based applications through the integration of existing technologies. Furthermore, we also define a methodology to use this tool efficiently. In order to evaluate the benefits of our work, a controlled experiment with eight developers (unfamiliar with ontologies) was performed to compare the proposed tool, JOINT, with another one, Jastor/Jena, frequently used by the community. The results suggest that our tool helps novice developers to create ontology-based applications faster and with few errors in the code. In addition, a real educational application with 10 ontologies, more than 200 ontology concepts (classes) and more than a million triples is already using the proposed tool successfully.