保留语义约束的关系模式到XML Schema转换

针对大量Internet应用程序需要将关系数据高效准确的转换发布为XML数据的问题,提出了从关系数据库模式到XML Schema的转换规则.本文通过分析目标数据库的关系模式和关联约束,归纳其在XML Schema中的等价形式,将目标数据库的结构和语义约束完整的保留到XML Schema中,并使转换后的XML Schema达到高度的嵌套.     

模糊OWL 2本体到模糊关系数据库映射形式化方法

针对语义Web上的模糊OWL 2本体,提出了基于关系数据库的模糊本体存储方法.通过深入分析模糊OWL 2本体的结构及语义特点,提出了基于模糊关系数据库的模糊本体存储结构,在此基础上,给出了模糊OWL 2本体到模糊关系数据库映射的形式化方法.理论和实例结果表明所提出的形式化映射方法是合理和可行的.     

对图书资料存储方式自动转换的探讨

在数字图书馆建设过程中,为有效利用早期直接存储于HTML表格中的图书资料信息,提出了一种将HTML表格转换为XML文档的方法。该方法利用HTML表格的格式化信息及其蕴涵的结构信息,对HTML表格的语义层次进行理解,然后将HTML表格数据转储于关系数据库中,最后利用关系数据库对XML文档的支持将数据库文件转换为XML文档。     

一种从关系数据库学习OWL本体的方法

提出了一种从关系数据库学习OWL本体的方法.该方法在形式化表示关系数据库模式和OWL本体的基础上,遵循从关系数据库模式到OWL本体的一组通用映射规则.基于Java 2平台的原型工具实现和案例研究证明了该方法的有效性.     

语义Web中本体构建的方法

通过分析语义Web的层次结构,指出本体在其中的核心作用.提出了基于统一建模语言类图可视化地表达本体并基于Web本体语言构建本体的方法,给出转换过程中的一些启发式规则;从一致性和结构合理性等方面给出了本体的评价准则;采用该方法有效地构建了选课本体,为语义Web中基于本体的知识表示和规则推理提供了切实可行的方法.     

基于抽取规则和本体映射的领域XML语义集成

油气井工程领域中存在大量的XML文档,传统的XML集成方案无法提供面向语义的信息查询,导致数据利用率不高。针对油气井XML文档WeXML语义集成与查询应用需求,提出一种基于抽取规则和本体映射的语义集成方法。首先定义一系列类、属性抽取规则,分别将WeXML Schema中的元素、属性映射为WeOWL本体中的类和属性;然后,利用实例转换算法将WeXML文档转换为本体实例数据;由于WeOWL提供有限的局部语义模型,需要在两者之间建立语义映射,并借助WeOWL中的术语对全局语义模型中的类和属性进行解释,进而提供面向领域全局本体的语义查询。通过构建WeXML数据语义集成原型系统,对提出的转换规则、转换算法和映射规则进行了验证。     

改进的关系模式本体学习映射规则研究

针对现有本体学习方法中存在的问题进行改进,提出了由关系模式进行本体学习的若干映射规则。首先介绍了本体的形式化定义,然后对现有关系模式向本体转换的各种方法进行了总结,并分析其存在的问题。在此基础上提出了基于关系模式的本体学习映射规则,并通过实例分别进行了说明。最后对算法的语义覆盖度进行了评价。     

关系数据库模式信息的提取

鉴于关系数据库是当前Web的主要信息源,关系数据库模式信息的提取是实现当前Web向语义Web迁移的基础技术之一,介绍关系数据库模式信息提取原型工具RDBSExtractor,分析与总结关系数据库的模式信息,给出基于JDBC API的RDBSExtractor设计思想和实现技术,案例研究表明。基于JDBC API的模式提取方法是可行的,RDBSExtractor是有效的。     

从OWL本体到关系数据库模式的转换

给出了OWL本体和关系数据库模式的形式定义;在分析了OWL本体和关系数据库模式之间概念对应的基础上,定义了从OWL本体到关系数据库模式的转换规则.基于J2SE平台的算法实现和典型实例表明,从OWL本体到关系数据库模式的转换是可行的.     

基于数据库的模糊本体存储方法研究

为解决语义网中模糊信息的表示问题,提出了一种基于关系数据库的模糊本体存储方法,定义了推理机制,实现成员关系的推理和关系度的计算.实验采用PostgreSQL数据库存储本体及SPARQL语言进行检索,结果证明了该方法的可行性.     

Bridge Ontology： A Multi-Ontologies.Based Approach for Semantic Annotation

Representing the relationships between ontologies is the key problem of semantic annotations based on multi ontologies. Traditional approaches only had the ability of denoting the simple concept subsumption relations between ontologies. Through analyzing and classifying the relationships between ontologies, the idea of bridge ontology was proposed, which had the powerful capability of expressing the complex relationships between concepts and relationships between relations in muhi ontologies. Meanwhile, a new approach employing bridge ontology was proposed to deal with the multi ontologies-based semantic annotation problem. The bridge ontology is a peculiar ontology, which can be created and maintained conveniently, and is effective in the multi-ontologies based semantic annotation. The approach using bridge ontology has the advantages of low cost, scalablc, robust in the web circumstance, and avoiding the unnecessary ontology extending and integration.     

基于HS-树的所有MIPS求解算法

本体在语义web中扮演很重要的角色.本体的重用、合并或者进一步扩展都有可能导致本体不一致.目前很多解决本体不一致的方法都需要计算最小不一致保持子集(MIPS).本文利用HS-树方法给出了一种能够有效计算本体中所有MIPS的算法.实验表明该算法是有效的,尤其对于本体中包含不可满足的概念远远多于MIPS的情形更加有效.     

Application of Improved Compact Particle Swarm Optimization to Large Ontology Alignment Task

Ontology occupies an important position in artificial intelligence, computer linguistics and knowledge management. However, when different ontologies are constructed to represent the same information in a domain, the so-called heterogeneity problem arises. In order to address this problem, a key task is to discover the semantic relationship of entities between given two ontologies, called ontology alignment. Recently, the meta-heuristic algorithms have already been regarded as an effective approach for solving ontology alignment problem. However, firstly, as the ontologies become increasingly large, meta-heuristic algorithms may be easier to find local optimal alignment in large search spaces. Secondly, many existing approaches exploit the population-based meta-heuristic algorithms so that the massive calculation is required. In this paper, an improved compact particle swarm algorithm by using a local search strategy is proposed, called LSCPSOA, to improve the performance of finding more correct correspondences. In LSCPSOA, two update strategies with local search capability are employed to avoid falling into a local optimal alignment. The proposed algorithm has been evaluated on several large ontology data sets and compared with existing ontology alignment methods. The experimental results show that the proposed algorithm can find more correct correspondences and improves the time performance compared with other meta-heuristic algorithms.     

支持语义查询的扩展关系型数据库设计及应用

传统的数据库非常适合于存储和处理大量的数据,但其检索是基于关键字的。本体的检索是基于知识的,但当实际的应用中存在大量的实例信息时,推理机都不能够得到特别满意的性能结果。吸取两者的优点,提出扩展关系型数据库检索模型,将其应用于数字博物馆的检索系统中,并对此方法检索的查全率和检索效率进行了分析。     

地理时空本体研究进展

地理现实的动态性早已被人们所广泛接受,日前关于时空GIS的研究也越来越多．本体技术的引进,为地理时空研究提供了一条新途径．从地理时空本体的起源基础介绍了时间本体、空间本体的基元、特性、表示模型等,综述了目前时空本体的研究进展,总结了目前研究中存在的一些问题并展望了未来的发展方向．     

本体上的代数结构

建立了本体概念集合上的一种代数结构——本体格,通过本体格上的代数性质分析本体结构的特性．由于本体的形式化研究仍处于初级阶段,尚缺乏对本体的严格的评价技术．代数方法是一种带有严谨逻辑推理的方法,代数性质是一种适用广泛的性质,本体上的代数性质可用于分析所有本体结构的特性．所以,建立的本体格为本体论的研究提供了一种具体的评价技术．     

Utilizing Statistical Semantic Similarity Techniques for Ontology Mapping——with Applications to AEC Standard Models

The objective of this paper is to introduce three semi-automated approaches for ontology mapping using relatedness analysis techniques. In the architecture, engineering, and construction (AEC) industry, there exist a number of ontological standards to describe the semantics of building models. Although the standards share similar scopes of interest, the task of comparing and mapping concepts among standards is challenging due to their differences in terminologies and perspectives. Ontology mapping is therefore necessary to achieve information interoperability, which allows two or more information sources to exchange data and to re-use the data for further purposes. The attribute-based approach, corpus-based approach, and name-based approach presented in this paper adopt the statistical relatedness analysis techniques to discover related concepts from heterogeneous ontologies. A pilot study is conducted on IFC and CIS/2 ontologies to evaluate the approaches. Preliminary results show that the attribute-based approach outperforms the other two approaches in terms of precision and F-measure.     

Utilizing Statistical Semantic Similarity Techniques for Ontology Mapping——with Applications to AEC Standard Models

The objective of this paper is to introduce three semi-automated approaches for ontology mapping using relatedness analysis techniques. In the architecture, engineering, and construction (AEC) industry, there exist a number of ontological standards to describe the semantics of building models. Although the standards share similar scopes of interest, the task of comparing and mapping concepts among standards is challenging due to their differences in terminologies and perspectives. Ontology mapping is therefore necessary to achieve information interoperability, which allows two or more information sources to exchange data and to re-use the data for further purposes. The attribute-based approach, corpus-based approach, and name-based approach presented in this paper adopt the statistical relatedness analysis techniques to discover related concepts from heterogeneous ontologies. A pilot study is conducted on IFC and CIS/2 ontologies to evaluate the approaches. Preliminary results show that the attribute-based approach outperforms the other two approaches in terms of precision and F-measure.     

一种面向DL-Lite本体原子断言的不一致度量算法

本体在语义web中扮演很重要的角色.本体的重用、合并或者进一步扩展都有可能导致本体不一致.度量不一致可以为解决本体不一致提供一些额外的信息.目前很多度量不一致度的方法都是基于复杂的描述逻辑,不适合具有大数据量的本体.本文从简单的描述逻辑DL-Lite入手,通过分析DL-Lite的特点,给出了原子断言不一致度的定义和一种能够有效计算原子断言不一致度的算法.算法复杂度的分析及实际的应用表明该方法是有意义的.