Integrating ontologies and thesauri for RDF schema creation and metadata querying

In this paper we present a new approach for building metadata schemas by integrating existing ontologies and structured vocabularies (thesauri). This integration is based on the specification of inclusion relationships between thesaurus terms and ontology concepts and results in application-specific metadata schemas incorporating the structural views of ontologies and the deep classification schemes provided by thesauri. We will also show how the result of this integration can be used for RDF schema creation and metadata querying. In our context, (metadata) queries exploit the inclusion semantics of term relationships, which introduces some recursion. We will present a fairly simple database-oriented solution for querying such metadata which avoids a (recursive) tree traversal and is based on a linear encoding of thesaurus hierarchies. Published online: 22 September 2000     

Bridging Ontologies and Conceptual Schemas in Geographic Information Integration

Integration of geographic information has increased in importance because of new possibilities arising from the interconnected world and the increasing availability of geographic information. Ontologies support the creation of conceptual models and help with information integration. In this paper, we propose a way to link the formal representation of semantics (i.e., ontologies) to conceptual schemas describing information stored in databases. The main result is a formal framework that explains a mapping between a spatial ontology and a geographic conceptual schema. The mapping of ontologies to conceptual schemas is made using three different levels of abstraction: formal, domain, and application levels. At the formal level, highly abstract concepts are used to express the schema and the ontologies. At the domain level, the schema is regarded as an instance of a generic data model. At the application level, we focus on the particular case of geographic applications. We also discuss the influence of ontologies in both the traditional and geographic systems development methodologies, with an emphasis on the conceptual design phase.     

利用本体和主题词表的集成构造RDF模式

为了减少语义异构性带来的信息发现、集成和存取的困难,论述了语义元数据构造,提出了通过集成现存的本体和主题词表构造元数据模式的一种新方法,即元数据模式构造的两步方法:在主题词T和本体O之间的连接关系规范;概念主题词表的自动构造。这个集成基于主题词术语和本体概念之间的蕴含关系规范,并产生具体应用的元数据模式,同时也给出利用结果元数据模式构造RDF模式的过程。     

OWL本体到关系数据库模式的映射

通过对现有本体存储模式的分析,以及对OWl本体和关系数据库模式之间的概念对应关系的分析,定义了从OWL本体到关系数据库模式的转换规则,给出了OWL本体存储模式的设计原则,并基于该原则提出了一种新的本体存储模式.针对本体描述语言OWL的构词特点,该模式提出将本体中的语义信息存放在不同的表中,以达到方便理解、结构稳定和提高效率的目的,适应于多数中小型本体的存储.     

Reasoning of fuzzy relational databases with fuzzy ontologies

A significant interest developed regarding the problem of describing databases with expressive knowledge representation techniques in recent years, so that database reasoning may be handled intelligently. Therefore, it is possible and meaningful to investigate how to reason on fuzzy relational databases (FRDBs) with fuzzy ontologies. In this paper, we first propose a formal approach and an automated tool for constructing fuzzy ontologies from FRDBs, and then we study how to reason on FRDBs with constructed fuzzy ontologies. First, we give their respective formal definitions of FRDBs and fuzzy Web Ontology Language (OWL) ontologies. On the basis of this, we propose a formal approach that can directly transform an FRDB (including its schema and data information) into a fuzzy OWL ontology (consisting of the fuzzy ontology structure and instance). Furthermore, following the proposed approach, we implement a prototype construction tool called FRDB2FOnto. Finally, based on the constructed fuzzy OWL ontologies, we investigate how to reason on FRDBs (e.g., consistency, satisfiability, subsumption, and redundancy) through the reasoning mechanism of fuzzy OWL ontologies, so that the reasoning of FRDBs may be done automatically by means of the existing fuzzy ontology reasoner.© 2012 Wiley Periodicals, Inc.     

An Ontology-Based Framework for Semi-Automatic Schema Integration

Currently, schema integration frameworks use approaches like rule-based, machine learning, etc. This paper presents an ontology-based wrapper-mediator framework that uses both the rule-based and machine learning strategies at the same time. The proposed framework uses global and local ontologies for resolving syntactic and semantic heterogeneity, and XML for interoperability. The concepts in the candidate schemas are merged on the basis of the similarity coefficient, which is calculated using the defined rules and the prior mappings stored in the case-base.     

MDSM: Microarray database schema matching using the Hungarian method

Current microarray databases use different terminologies and structures and thereby limit the sharing of data and collating of results between laboratories. Consequently, an effective integrated microarray data model is required. One important process to develop such an integrated database is schema matching. In this paper, we propose an effective schema matching approach called MDSM, to syntactically and semantically map attributes of different microarray schemas. The contribution from this work will be used later to create microarray global schemas. Since microarray data is complex, we use microarray ontology to improve the measuring accuracy of the similarity between attributes. The similarity relations can be represented as weighted bipartite graphs. We determine the best schema matching by computing the optimal matching in a bipartite graph using the Hungarian optimisation method. Experimental results show that our schema matching approach is effective and flexible to use in different kinds of database models such as; database schema, XML schema, and web site map. Finally, a case study on an existing public microarray schema is carried out using the proposed method.     

基于本体的关系数据集成的查询处理

为实现异构关系数据库的语义集成,针对传统集成技术存在的问题,在对语义网等相关技术进行分析的基础上,研究基于本体的关系数据集成系统中的查询处理问题,提出了一种基于本体的关系数据库集成框架。设计了基于本体的关系数据的描述方法,使用本体作为集成的全局模式来描述关系模式的语义。设计了查询重写算法,该算法可以将基于全局模式的SPARQL查询重写为针对具体关系数据库的查询,从而实现对异构关系数据库的集成。实验表明,该算法具有良好的可扩展性。     

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.     

一种基于部分已验证匹配关系的模式匹配模型

模式匹配是模式集成、语义WEB及电子商务等领域的重点及难点问题. 为了有效利用专家知识提高匹配质量, 提出了一种基于部分已验证匹配关系的模式匹配模型. 在该模型中, 首先,人工验证待匹配模式元素间的少量对应关系, 进而推理出当前任务下部分已知的匹配关系及单独匹配器的缺省权重; 然后,基于上述已收集到的先验知识对多种匹配器所生成的相似度矩阵进行合并及调整, 并在全局范围内进行优化; 最后,对优化矩阵的选择性进行评估, 从而为不同匹配任务推荐最合理的候选匹配生成方案. 实验结果表明, 部分已验证匹配关系的使用有助于模式匹配质量的提高.     

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.     

信息网格中基于本体的信息共享全局视图构建方法研究

信息共享对于增强信息网格单元的协作能力和快速反应能力起着至关重要的作用．从解决信息网格语义异构问题出发，为实现网格信息共享，提出了一种基于本体的信息网格全局视图构建方法．该方法主要由两部分核心内容组成：全局本体的生成和基于全局本体的全局视图构建．首先深入研究了信息网格全局本体的生成；基于全局本体透彻分析了如何构建信息共享全局视图；结合具体实例详细说明了所提出信息网格全局视图构建方法的工作流程与应用．对于相关工作进行了分析比较，最后给出了结论和下一步工作．     

Interschema correspondence establishment in a cooperative OWL-based multi-information server grid environment

Establishing interschema semantic knowledge between corresponding elements in a cooperating OWL-based multi-information server grid environment requires deep knowledge, not only about the structure of the data represented in each server, but also about the commonly occurring differences in the intended semantics of this data. The same information could be represented in various incompatible structures, and more importantly the same structure could be used to represent data with many diverse and incompatible semantics. In a grid environment interschema semantic knowledge can only be detected if both the structural and semantic properties of the schemas of the cooperating servers are made explicit and formally represented in a way that a computer system can process. Unfortunately, very often there is lack of such knowledge and the underlying grid information servers (ISs) schemas, being semantically weak as a consequence of the limited expressiveness of traditional data models, do not help the acquisition of this knowledge. The solution to overcome this limitation is primarily to upgrade the semantic level of the IS local schemas through a semantic enrichment process by augmenting the local schemas of grid ISs to semantically enriched schema models, then to use these models in detecting and representing correspondences between classes belonging to different schemas. In this paper, we investigate the possibility of using OWL-based domain ontologies both for building semantically rich schema models, and for expressing interschema knowledge and reasoning about it. We believe that the use of OWL/RDF in this setting has two important advantages. On the one hand, it enables a semantic approach for interschema knowledge specification, by concentrating on expressing conceptual and semantic correspondences between both the conceptual (intensional) definition and the set of instances (extension) of classes represented in different schemas. On the other hand, it is exactly this semantic nature of our approach that allows us to devise reasoning mechanisms for discovering and reusing interschema knowledge when the need arises to compare and combine it.     

Semantics based customization of UBL document schemas

Universal Business Language (UBL) is an OASIS initiative to develop common business document schemas to provide document interoperability in the eBusiness domain. Since the data requirements change according to a context, UBL schemas need to be customized and UBL defines a guideline to be followed for customization of schemas. XSD derivation based customization as proposed by UBL provides syntactic interoperability, that is, an XML parser that can interpret standard UBL documents can also interpret customized UBL documents. We argue that for UBL to become mainstream, syntactic interoperability alone is not enough. It needs to be supported by semantic interoperability, that is, it must be possible for users and even automated processes to discover and reuse customizations provided by other users. In this paper, we describe how to improve the UBL customization mechanism by providing semantic representations for context domains and describe how these semantics can be utilized by automated processes for component discovery and schema customization. For this purpose, we derive ontologies from taxonomies like the North American Industry Classification System (NAICS), the Universal Standard Products and Services Classification (UNSPSC) and relate corresponding concepts from different ontologies through ontology alignment. Then, we process these aligned ontologies using a reasoner to compute inferred ontologies representing context domains. We show that when custom UBL components are annotated using classes from these ontologies, automated discovery and customization becomes possible.     

Instance-based domain ontological view creation towards semantic integration

In many domains today there are very limited explicit ontologies established for implementing information systems. Traditional ontology-driven semantic integration approaches cannot be directly applied in integrating these information systems. Usually, the information systems have schemas, a type of formal information model, for their information repositories which to some extent imply the semantics of the information. Each schema actually reflects a specific view of the domain conceptualization. This paper investigates the theoretical foundation of ontologies and extends the traditional ontology concept to the ontological view concept. It proposes to use ontological views to address the challenge of semantic integration. The proposed approach adopts the schemas to create local ontological views, uses data instances of the information systems to discover semantic relationships between the concepts within the ontological views, and builds a domain ontological view based on the discovered equivalence mappings. It applies the hierarchical clustering technique on the data instances and, in the further analysis, uses the clusters to reduce the cost of processing a large amount of data. The matching of concept properties is based on the probability distribution of the data instances. The experimental results have demonstrated the effectiveness of this approach.     

基于GLAV集成中的模式匹配方法研究

基于GLAV集成的系统具体更好的伸缩性.本文研究了这种集成系统中的模式匹配方法,并基于模式树进行匹配,实现了全局模式与源模式之间的映射.     

A Discovery-Based Approach to Database Ontology Design

In this paper, we introduce an approach to task-driven ontology design which is based on information discovery from database schemas. Techniques for semi-automatically discovering terms and relationships used in the information space, denoting concepts, their properties and links are proposed, which are applied in two stages. At the first stage, the focus is on the discovery of heterogeneity/ambiguity of data representations in different schemas. For this purpose, schema elements are compared according to defined comparison features and similarity coefficients are evaluated. This stage produces a set of candidates for unification into ontology concepts. At the second stage, decisions are made on which candidates to unify into concepts and on how to relate concepts by semantic links. Ontology concepts and links can be accessed according to different perspectives, so that the ontology can serve different purposes, such as, providing a search space for powerful mechanisms for concept location, setting a basis for query formulation and processing, and establishing a reference for recognizing terminological relationships between elements in different schemas.     

Double-layered schema integration of heterogeneous XML sources

Schema integration aims to create a mediated schema as a unified representation of existing heterogeneous sources sharing a common application domain. These sources have been increasingly written in XML due to its versatility and expressive power. Unfortunately, these sources often use different elements and structures to express the same concepts and relations, thus causing substantial semantic and structural conflicts. Such a challenge impedes the creation of high-quality mediated schemas and has not been adequately addressed by existing integration methods. In this paper, we propose a novel method, named XINTOR, for automating the integration of heterogeneous schemas. Given a set of XML sources and a set of correspondences between the source schemas, our method aims to create a complete and minimal mediated schema: it completely captures all of the concepts and relations in the sources without duplication, provided that the concepts do not overlap. Our contributions are fourfold. First, we resolve structural conflicts inherent in the source schemas. Second, we introduce a new statistics-based measure, called path cohesion, for selecting concepts and relations to be a part of the mediated schema. The path cohesion is statistically computed based on multiple path quality dimensions such as average path length and path frequency. Third, we resolve semantic conflicts by augmenting the semantics of similar concepts with context-dependent information. Finally, we propose a novel double-layered mediated schema to retain a wider range of concepts and relations than existing mediated schemas, which are at best either complete or minimal, but not both. Performed on both real and synthetic datasets, our experimental results show that XINTOR outperforms existing methods with respect to (i) the mediated-schema quality using precision, recall, F-measure, and schema minimality; and (ii) the execution performance based on execution time and scale-up performance.