一种基于虚拟路径的本体匹配算法

本体匹配是实体之间关系的体现,不仅单个概念之间的关系影响本体的匹配关系,概念相邻元素及其语义联系对本体的匹配关系的影响也不容忽视。提出了基于虚拟路径的本体匹配新方法,通过为两个本体元素分别建立由具有语义联系的相部元素及其联系所构成的虚拟路径,将两个目标元素的虚拟路径中各独立要素分别对应地进行概念语义相似性比较;综合虚拟路径内各独立要素的概念语义相似性,获取两个目标元素虚拟路径的图形语义相似性;根据虚拟路径的图形语义相似性推导两个目标元素之间的映射关系。实验表明,该方法能够有效提高本体匹配的质量和性能。     

基于最大公共子图的本体映射方法研究

本体映射是对两个本体中的各元素建立语义关系,而影响本体映射的关键是相似度的计算方法。针对相似度计算方法中仍存在语义关系不精准的问题,提出一种本体映射方法,把本体映射问题转化为求解最大公共子图的问题。以图结构表示的本体可更好地体现本体结构之间潜在的语义关系,应用最大公共子图提取本体中的公共部分,并用最大公共子图的性质计算2个本体中元素之间的相似度,进而得到2个本体之间的映射关系。实验结果表明,与Ctx Match,COMA相比,该方法在召回率和准确率方面都有一定提高。     

基于概念图匹配的语义相似性算法研究

本文针对传统搜索技术查全率和查准率不能满足用户日益增长的需求这一突出问题,提出一种基于概念图语义匹配的方法来计算两个本体中类之间的相似性,文中提到的本体是由实体类、这些类之间的语义关系和描述这些类的不同特征组成的.该模型首先将用户的查询信息转变为一个概念图,然后和已有的资源概念图进行匹配计算语义的相似性,实例表明该方法可以满足用户的需求,提高了检索效率.     

制造资源本体的概念语义相似度研究

为了实现制造资源本体之间的语义互操作,对本体中的概念进行语义相似性计算为进行此操作的关键技术之一。本文提出了一种计算概念语义相似度的新方法,将概念语义相似度分为两部分：主体相似度和附加相似度。主体相似度综合考虑了概念自身的相似度,该概念的父概念和子概念间的相似度,以及概念间的二元关系,同时,加入了概念属性相似度,属性携带了概念的大部分语义信息,计算属性相似度可以有效提高概念语义相似度的准确性。附加相似性是指通过本体中概念的层次结构对主体相似度进行语义补充,利用概念的深度对得到的概念语义相似度进行语义调整,有效的弥补了仅仅利用主体相似度计算概念语义相似度的不足。最后,通过实例证明了该方法的有效性。     

受限本体相似

在从不同的语义Web上得到用本体表达的文档资源以后,这些文档资源通常被转换成基于同一个本体的本体描述,这样既便于对文档的分析,又便于在此基础上进行信息抽取.这些文档本体之间仅仅在实例和关系层上彼此相互不同,在类、属性、规则、谓词方面都基本相同.对这种文档的检索,一个最普通的操作就是计算本体之间的相似性.很多计算本体相似性的方法基本上都是以分别属于不同本体的实体之间配对比较来实现,而且往往要考虑所有相关的元素.这不仅增加了计算复杂度,还会遇到循环计算的问题.在对语义网本体语言的推理能力进行研究以后,提出了一种基于知识推理的二阶本体相似技术,解决了循环计算的问题.     

基于OWL的本体建模研究

计算机对语义Web上的文档理解是建立在本体基础之上的，本体是用来定义概念和数据之间的关系的，因此本体建模是非常重要的。介绍了本体与语义Web的关系，并以基于OWL的本体为例，对本体的建模以及相关问题进行研究。     

基于语义测度的图像相似性计算研究

针对图像检索中的低层视觉特征相似性度量问题,提出一种基于语义测度的图像相似性计算方法。该方法在图像区域分割的基础上,通过构建图像区域子块与语义元数据之间的统计映射关系,实现图像内容的统计语义描述,建立图像之间、图像与语义类别、语义类别之间的分层语义相似测度。通过对自然图像库的实验结果表明,该方法在相似图像检索中具有更好的性能。     

基于个性化本体的图像语义标注和检索

针对目前图像检索系统较难实现语义检索的问题,提出了一种新的以本体为核心的图像语义标注和检索模型。构建个性化本体描述图像语义,继而提取基于概念集的图像语义特征并利用本体中“Is-A”关系设计相似性度量方法最终实现语义扩展检索。其难点在于顶级本体向个性化本体进化,以及基于概念集和“Is-A”关系实现语义相似度量的方法。通过系统的初步实现与相关实验的验证,该模型的检索准确度可达88.6%,明显高于传统的基于关键字和基于通用本体的图像检索,实现了图像智能检索功能。     

一个基于语义元的相似度计算方法研究*

针对已有相似性度量方法的局限与不足,对属性进行语义扩展,提出了基于语义元支持度的相似度计算方法,该方法用语义元表示概念内涵,在语义元中引入支持度来表现不同语义元对概念表示的贡献,综合考虑相关性、相似性、非对称性以及语义元的支持度,通过比较语义元的相似性,实现了概念相似性的度量。把关系作为一种特殊的概念进行关系的比较,得到了基于语义元的本体相似性度量。最后,将该方法与其他方法进行比较,验证了该方法的计算结果更具有合理性,同时也验证了该方法的有效性与正确性。     

面向语义集成--本体在Web信息集成中的研究进展

本体（Ontology）是描述概念及概念之间关系的概念模型，通过概念之间的关系来描述概念的语义，作为一种有效表现概念层次结构和语义的模型，本体在Web信息集成中得到广泛的应用，文中就目前本体在Web信息集成中的应用做了分析与探讨，以期对相关领域的同行有参考作用。     

Extracting semantic relations to enrich domain ontologies

Domain ontologies facilitate the organization, sharing and reuse of domain knowledge, and enable various vertical domain applications to operate successfully. Most methods for automatically constructing ontologies focus on taxonomic relations, such as is-kind-of and is-part-of relations. However, much of the domain-specific semantics is ignored. This work proposes a semi-unsupervised approach for extracting semantic relations from domain-specific text documents. The approach effectively utilizes text mining and existing taxonomic relations in domain ontologies to discover candidate keywords that can represent semantic relations. A preliminary experiment on the natural science domain (Taiwan K9 education) indicates that the proposed method yields valuable recommendations. This work enriches domain ontologies by adding distilled semantics.     

时空本体及其逻辑基础研究现状与展望

总结了时空本体及其逻辑基础--时空描述逻辑的研究工作.指出了时空本体和时空描述逻辑的主要问题是复杂度过高(一般情况下不可判定)、不支持多元的时空关系和时空关系复合推理.解决方案是将时空推理和描述逻辑、本体充分结合,建立表达和推理能力更强的时空本体.     

Resolution of Semantic Heterogeneity in Database Schema Integration Using Formal Ontologies

This paper addresses the problem of handling semantic heterogeneity during database schema integration. We focus on the semantics of terms used as identifiers in schema definitions. Our solution does not rely on the names of the schema elements or the structure of the schemas. Instead, we utilize formal ontologies consisting of intensional definitions of terms represented in a logical language. The approach is based on similarity relations between intensional definitions in different ontologies. We present the definitions of similarity relations based on intensional definitions in formal ontologies. The extensional consequences of intensional relations are addressed. The paper shows how similarity relations are discovered by a reasoning system using a higher-level ontology. These similarity relations are then used to derive an integrated schema in two steps. First, we show how to use similarity relations to generate the class hierarchy of the global schema. Second, we explain how to enhance the class definitions with attributes. This approach reduces the cost of generating or re-generating global schemas for tightly-coupled federated databases.     

Automatic construction and enrichment of informal ontologies: A survey

The conceptualization of knowledge required for an efficient processing of textual data is usually represented as ontologies. Depending on the knowledge domain and tasks, different types of ontologies are constructed: formal ontologies, which involve axioms and detailed relations between concepts; taxonomies, which are hierarchically organized concepts; and informal ontologies, such as Internet encyclopedias created and maintained by user communities. Manual construction of ontologies is a time-consuming and costly process requiring the participation of experts; therefore, in recent years, there have appeared many systems that automate this process in a greater or lesser degree. This paper provides an overview of methods for automatic construction and enrichment of ontologies, with the focus being placed on informal ontologies.     

Semi-automatic construction of domain ontology for agent reasoning

One of the key elements of the Semantic Web technologies is domain ontologies and those ontologies are important constructs for multi-agent system. The Semantic Web relies on domain ontologies that structure underlying data enabling comprehensive and transportable machine understanding. It takes so much time and efforts to construct domain ontologies because these ontologies can be manually made by domain experts and knowledge engineers. To solve these problems, there have been many researches to semi-automatically construct ontologies. Most of the researches focused on relation extraction part but manually selected terms for ontologies. These researches have some problems. In this paper, we propose a hybrid method to extract relations from domain documents which combines a named relation approach and an unnamed relation approach. Our named relation approach is based on the Hearst’s pattern and the Snowball system. We merge a generalized pattern scheme into their methods. In our unnamed relation approach, we extract unnamed relations using association rules and clustering method. Moreover, we recommend candidate relation names of unnamed relations. We evaluate our proposed method by using Ziff document set offered by TREC.     

基于本体的ETL设计研究

提出了一种基于本体的ETL设计方法,通过建立各数据源的局部本体和目标数据仓库的全局本体以及本体间的映射,得出以OWL表示的各数据源和目标的映射关系.用本体元数据指导数据抽取,转换和加载过程,解决数据源ETL过程中的语义异构问题,实现了企业数据语义程度的集成.     

Enabling Cross-Disciplinary E-Science by Integrating Geoscience Ontologies with Dolce

E-Science is increasingly being used to address scientific problems that require cross-disciplinary knowledge, such as climate change, natural disasters, and environmental health. However, the ontologies used to represent scientific knowledge are largely unidisciplinary and need to be integrated to enable big e-Science. The authors investigate the potential of the Dolce foundational ontology to aid the integration of two geoscientific knowledge representations, the Sweet ontology and the GeoSciML schema, to meet the requirements of a cross-disciplinary use case focused on groundwater pollution estimation. They connected the domain ontologies via the foundational ontology, leading to new and improved relations between the domain ontologies that enabled satisfaction of the use case. Although the integrated ontology, called Dolce Rocks, contains some semantic inconsistencies resulting from incompatibilities among the ontologies, the overall results suggest that foundational ontologies can play an important role in cross-disciplinary e-Science.