Using Bayesian decision for ontology mapping

Ontology mapping is the key point to reach interoperability over ontologies. In semantic web environment, ontologies are usually distributed and heterogeneous and thus it is necessary to find the mapping between them before processing across them. Many efforts have been conducted to automate the discovery of ontology mapping. However, some problems are still evident. In this paper, ontology mapping is formalized as a problem of decision making. In this way, discovery of optimal mapping is cast as finding the decision with minimal risk. An approach called Risk Minimization based Ontology Mapping (RiMOM) is proposed, which automates the process of discoveries on 1:1, n:1, 1:null and null:1 mappings. Based on the techniques of normalization and NLP, the problem of instance heterogeneity in ontology mapping is resolved to a certain extent. To deal with the problem of name conflict in mapping process, we use thesaurus and statistical technique. Experimental results indicate that the proposed method can significantly outperform the baseline methods, and also obtains improvement over the existing methods.     

一种综合的本体相似度计算方法

本体相似度计算是本体映射的关键环节.本体的实例、关系、属性、结构等信息是相似度计算需要考虑的重要因素.针对目前本体映射过程中相似度计算所存在的问题,提出了一种综合的相似度计算方法.首先判断不同本体之间是否存在相关性.若相关,则充分考虑各种相关因素,从语义和概念两个层面来进行比较,然后给出了本体的综合相似度计算方法.最后采用了两组测试数据对该方法进行实验,并与GLUE系统的概率统计方法进行了实验对比.实验结果表明,该方法能够有效确保相似度计算的准确性.     

Reusing ontology mappings for query routing in semantic peer-to-peer environment

To efficiently support automated interoperability between ontology-based information systems in distributed environments, the semantic heterogeneity problem has to be dealt with. To do so, traditional approaches have acquired and employed explicit mappings between the corresponding ontologies. Usually these mappings can be only obtained from human domain experts. However, it is too expensive and time-consuming to collect all possible mapping results on distributed information systems. More seriously, as the number of systems in a large-scale peer-to-peer (P2P) network increases, the efficiency of the ontology mapping is exponentially decreased. Thereby, in this paper, we propose a novel semantic P2P system, which is capable of (i) sharing and exchanging existing mappings among peers, and (ii) composing shared mappings to build a certain path between two systems. Given two arbitrary peers (i.e., source and destination), the proposed system can provide indirect ontology mappings to make them interoperable. In particular, we have focused on query-based communication for evaluating the proposed ontology mapping composition system. Once direct ontology mappings are collected from candidate peers, a given query can be (i) segmented into a set of sub-queries, and (ii) transformed to another query. With respect to the precision performance, our experimentation has shown an improvement of about 42.5% compared to the keyword-based query searching method.     

本体集成研究综述

本体集成(ontology integration)的目的是使异质的本体互操作,目前是本体研究的一个热点.本体集成首先发现实体间关系,生成本体映射,然后根据应用目的基于映射进行处理,最终达成本体对齐或者本体合并的目标.本文介绍了本体集成中的概念,给出本体集成的一般工程化方法.对国内外较具代表性的本体集成工具进行比较分析,讨论了现存的问题,指出了未来的研究方向.     

基于SWRL规则的简单对等本体关联方法研究与实现

提出的"基于SWRL规则的简单对等本体关联方法"通过对SWRL规则的应用和扩充实现多个简单对等本体之间在类上的对应关联,从而可以达到构建简单的本体并通过本体之间的关联来获取更大的、更多的知识的目的.本法为本体的重用和映射提供了有效的方法.     

本体复合映射研究

已有的本体复合映射算法中,绝大部分都只能发现基于字符串比较的简单类型的复合映射,查准率和查全率不高。提出一种基于结点间包含关系和等价关系发现复合映射的方法。实验证明,该方法可以发现本体中包含的绝大部分复合映射,较之现有的复合映射发现算法在查准率和查全率方面有了很大改进。     

Learning to match ontologies on the Semantic Web

On the Semantic Web, data will inevitably come from many different ontologies, and information processing across ontologies is not possible without knowing the semantic mappings between them. Manually finding such mappings is tedious, error-prone, and clearly not possible on the Web scale. Hence the development of tools to assist in the ontology mapping process is crucial to the success of the Semantic Web. We describe GLUE, a system that employs machine learning techniques to find such mappings. Given two ontologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well-founded probabilistic definitions to several practical similarity measures and show that GLUE can work with all of them. Another key feature of GLUE is that it uses multiple learning strategies, each of which exploits well a different type of information either in the data instances or in the taxonomic structure of the ontologies. To further improve matching accuracy, we extend GLUE to incorporate commonsense knowledge and domain constraints into the matching process. Our approach is thus distinguished in that it works with a variety of well-defined similarity notions and that it efficiently incorporates multiple types of knowledge. We describe a set of experiments on several real-world domains and show that GLUE proposes highly accurate semantic mappings. Finally, we extend GLUE to find complex mappings between ontologies and describe experiments that show the promise of the approach.Received: 16 December 2002, Accepted: 16 April 2003, Published online: 17 September 2003Edited by: Edited by B.V. Atluri, A. Joshi, and Y. Yesha     

基于范畴论的本体描述方法

针对各种本体语言在描述本体过程中容易产生理解不一致的问题,利用范畴论的抽象性及图形化表示的优势,提出一种基于范畴论的本体描述方法,并给出了本体、本体映射和本体实例化的范畴论定义,其中本体被描述成范畴中的对象,本体间的映射被描述成范畴中对象间的同态射,本体实例化被描述成范畴间的函子。在此基础上,结合共极限和推出给出本体合并的描述及解释,并证明了本体合并的一些典型性质。     

本体校正的半自动处理过程

创建本体间的映射是个单调冗烦的工作过程,特别是当各本体非常庞大时。从Ontol-ogy的基本理论出发,基于基本概念、角色和关系,探讨了Ontology开发中的各个要素,然后介绍了一种半自动校正处理过程,该过程是在本体校正框架的基础上实现的。由即将被校正的两个本体组成输入处理过程,而输出处理过程则是本体间实体的通信集合。     

本体相似度研究

不同本体之间的交互成为语义Web的首要任务,其中本体相似度计算是本体映射的关健环节。在以往的研究中,本体相似度计算通常专注于模式及其结构的匹配。目前研究朝着进一步考虑本体内部语义信息方向努力。本文描述了语义相似度栈的各个层次,依据各个层次的语义特征对目前本体相似度方法进行分类,并对每种方法进行了详细描述。最后对现有一些主要的本体间相似度计算方法进行归纳总结。这项研究工作将为人们提出新的相似度方法或者组合的计算方法作一个参考。     

XML模式到本体的提升——概念搜索算法

针对XML在表示非结构化信息时语义表达能力不足,通过建立XML模式和本体之间的关联,提出了一种新的算法--概念搜索算法,此算法可在XML模式的元素和本体概念之间构建映射;在此基础上给出了一个初步的评价结果,并利用这个评价结果将来自B2B通信的真实世界模式映射到使用这个算法的不同本体.     

How do we measure and improve the quality of a hierarchical ontology?

Hierarchical ontologies enable organising information in a human-machine understandable form, but constructing them for reuse and maintainability remains difficult. Often supporting tools available lack formal methodological underpinning and their developers are not supported by any concomitant metrics. The paper presents a formal underpinning to provide quality metrics of a taxonomy hierarchical ontology and proposes a methodology for semi-automatic building of maintainable taxonomies. Users provide terms to be used to describe different ontological elements as well as their attributes and their ranges of values. The methodology uses the formalised metrics to assess the quality of the users input and proposes changes according to given quality constraints. The paper illustrates the metrics and the methodology in constructing and repairing two medium size well-known taxonomies.     

关系数据库模式和本体间映射的研究综述

关系数据库模式和本体问映射是语义网研究中的一个重要问题.首先,给出关系数据库模式和本体间映射的形式化定义,并从建模思想和应用场景两个方面分析问题的难点.根据3个不同角度,即模型转换的途径、映射策略的适用范围以及映射结果的表达形式,调研当前存在的多种解决途径.在此基础上,进一步介绍并比较6个具有代表性的关系数据库模式和本体间映射的工具.最后,讨论存在的挑战,并指出未来可能的研究方向.     

Requirements-oriented methodology for evaluating ontologies

Many applications benefit from the use of a suitable ontology but it can be difficult to determine which ontology is best suited to a particular application. Although ontology evaluation techniques are improving as more measures and methodologies are proposed, the literature contains few specific examples of cohesive evaluation activity that links ontologies, applications and their requirements, and measures and methodologies. In this paper, we present ROMEO, a requirements-oriented methodology for evaluating ontologies, and apply it to the task of evaluating the suitability of some general ontologies (variants of sub-domains of the Wikipedia category structure) for supporting browsing in Wikipedia. The ROMEO methodology identifies requirements that an ontology must satisfy, and maps these requirements to evaluation measures. We validate part of this mapping with a task-based evaluation method involving users, and report on our findings from this user study.     

本体进化及维护的研究总结

本体是共享概念的规范、精确的描述。本体本身并不是静态的模型,所以它必须能够捕获意思和关系的变化。这样,本体的映射和进化就成为本体学习和发展的一个基本任务。本体进化就是获得新的信息和知识时适当地维护和扩充已有本体。     

基于多映射的本体合并框架设计

由于分布式环境下构建的本体异构特性,严重阻碍了本体间的互操作性,对同一领域本体进行合并是解决本体异构的最有效方法之一。本文提出了一种基于多映射的本体合并框架,在结合当前映射技术优点的基础之上,配备语义桥生成器和冲突处理器,能充分提高合并的效率,该框架可以在语义Web中将源本体自动合并生成新的目标本体。     

Detection and resolution of semantic inconsistency and redundancy in an automatic ontology merging system

In recent years, researchers have been developing algorithms for the automatic mapping and merging of ontologies to meet the demands of interoperability between heterogeneous and distributed information systems. But, still state-of-the-art ontology mapping and merging systems is semi-automatic that reduces the burden of manual creation and maintenance of mappings, and need human intervention for their validation. The contribution presented in this paper makes human intervention one step more down by automatically identifying semantic inconsistencies in the early stages of ontology merging. We are detecting semantic heterogeneities that occur due to conflicts among the set of Generalized Concept Inclusions, Property Subsumption Criteria, and Constraint Satisfaction Mechanism in local heterogeneous ontologies, which become obstacles for the generation of semantically consistent global merged ontology. We present several algorithms to detect such semantic inconsistencies based on subsumption analysis of concepts and properties in local ontologies from the list of initial mappings. We provide ontological patterns for resolving these inconsistencies automatically. This results global merged ontology free from ??circulatory error in class/property hierarchy??, ??common class between disjoint classes/properties??, ??redundancy of subclass/subproperty of relations?? and other types of ??semantic inconsistency?? errors. Experiments on the real ontologies show that our algorithms save time and cost of traversing local ontologies, improve system??s performance by producing only consistent accurate mappings, and reduce the users?? dependability for ensuring the satisfiability of merged ontology.     

Empirical findings on ontology metrics

Ontologies are becoming the preferred way of representing, dealing and reasoning with large volumes of information in several domains. In consequence, the creation, evaluation and maintenance of ontologies has become an engineering process that needs to be managed and measured using sound and reliable methods. As part of any ontology engineering or revision process, metrics can play a role helping in identifying possible problems or incorrect use of ontology elements, along with providing a kind of quality assessment that complements reviews requiring expert inspection. However, in spite of the fact that there are a number of ontology metric proposals described in the literature, there is a lack of empirical studies that provide a basis for their interpretation. This paper reports a systematic exploration of existing ontology metrics from a large set of ontologies extracted from the Swoogle search engine. The OntoRank value used inside the Swoogle to rank search results is used as a contrast for some existing metrics. The results show that the existing proposed metrics evaluated in general do not seem to be indicators of that ranking, but they can be helpful to identify particular kinds of ontologies.     

不同本体中概念语义距离的一种度量方法

本体合并与映射中的一个重要步骤是检测不同本体中的相似概念,以提供合并或者映射的作用点.通常,具有相似名称的概念常被作为候选.然而,完全有可能出现具有文字相似名称的概念在语义上却不相似,甚至不相容.本文提出了一个从四方面比较概念语义距离的方法,综合考虑了一个概念在本体中的位置以及概念的属性这些对于确定概念涵义非常重要的信息.