基于领域本体和关系模型的XML语义集成方法研究

由于缺乏足够的语义信息,不同模式的XML数据之间很难进行互操作。针对油气井工程中的XML数据集成需求,借助领域全局本体,提出一种模式无关的XML语义集成方法。该方法首先在XML Path路径与领域本体之间进行语义映射,屏蔽其模式差异;然后,按照模型映射方法将XML存储为关系数据;最后通过查询重写将SPARQL转换为SQL语句,实现语义查询。该方法对XML模式进行语义标注,利用关系数据库存储与查询XML数据,能有效处理领域XML数据的语义集成。     

异构专利数据源集成系统中查询的研究

为了有效地对异构专利数据源进行统一的查询,提出一个基于本体的异构专利数据源集成系统.该系统引入本体解决数据源集成中存在的语义异构,通过全局数据模式为用户提供统一的查询接口,将用户针对全局数据模式的查询重写为针对各个局部数据源的子查询.使用该系统,用户可以从异构的专利源中得到正确的查询结果.     

一种基于Ontology的数据集成系统

针对异构数据源中实现基于语义的数据集成的需求，采用分层思想，在用户层与实际数据层之间增加一个中间层来屏蔽底层数据的异构，用本体作为公共语义描述工具，建立本体到各数据源的映射规则，设计实现了将基于本体的全局查询转化为基于各数据源的局部查询系统，解决了数据源之间的语义异构问题，其中用OWL表示本体和映射，并研究了系统中的关键技术。     

基于本体的专利数据源集成的研究及应用

分析了异构专利数据源集成中存在的困难,针对不同专利数据源间存在的分布性、自治性、异构性等问题,提出了一个基于混合本体的专利数据源集成解决方案.该方案采用局部本体描述单个专利数据源中的语义,通过本体合并构建全局本体实现多个专利数据源问的语义集成,定义全局本体与局部本体之间的映射关系解决多个异构数据源集成中存在的语义异构问题.使用该方案,用户可从集成的专利数据源中获取正确的查询结果,有效地解决了"信息孤岛"问题.     

基于本体的数据集成系统的设计与实现

针对当前对异构数据源的数据集成中存在的语义异构问题,尤其是元素层的语义异构,通过分析现有的基于XML数据集成方法的基础上,将系统的设计分为三层,其中在数据层实现对异构数据源XML模式的包装,而在用户层和数据层之间增加一个中间层来实现全局查询到局部查询的转换。同时将本体论的思想引入集成系统框架,通过在中间层建立全局本体库和局部本体库,以及构建本体库之间的映射关系,解决了数据集成中异构数据源元素层的语义异构问题。     

基于本体的XML语义集成研究

针对具有相同语义的XML数据经常具有不同表达形式的问题,采用了基于本体的语义集成方法来集成异构XML数据源,即通过一系列映射规则将XPath的局部路径与本体关联起来,将用户的RDQL查询重写为基于XML的XQuery查询,从而达到语义集成的效果。本方法的意义在于用户可以通过本体查询异构的XML数据源。     

数据库语义合取查询研究

针对异质、异构数据库的语义集成中,对海量元组进行语义查询时因效率问题而无法使用丰富的语义表达能力的问题,提出一种兼顾速度和语义表达能力的算法,将语义查询和本体都进行图形化表示,并实现子图的语义匹配,将匹配的结果转化成数据库查询语句。与将语义查询直接重写为SQL的算法相比,该算法能支持更丰富的语义。     

基于本体的XML语义集成研究

针对具有相同语义的XML数据经常具有不同表达形式的问题，采用了基于本体的语义集成方法来集成异构XML数据源，即通过一系列映射规则将XPath的局部路径与本体关联起来，将用户的RDQL查询重写为基于XML的XQuery查询，从而达到语义集成的效果。本方法的意义在于用户可以通过本体查询异构的XML数据源。     

一种面向领域的虚拟视图语义集成方法*

针对油井工程领域异构数据源,提出了一种基于语义的虚拟视图集成方法。通过本体抽取和本体合并分别构造数据源局部本体和全局本体实现基于语义的数据访问视图,并利用全局本体、局部本体和数据源模式之间的映射关系,将基于语义的查询转换为底层数据源的访问请求,提供统一、透明的数据访问视图,以避免数据加载和更新等问题。通过油井决策支持数据集成平台,该方法取得了很好的应用效果。     

一种关系数据库模式到本体映射的失效检测方法

从关系数据库到本体的语义映射对于实现关系数据库和本体之间的互操作具有十分重要的作用。然而,关系数据库模式和本体的演化都会造成这些语义映射的失效,只有准确定位受影响的映射集合才能进行进一步的映射维护工作。提出了一种映射失效检测方法,首先通过对关系数据库模式(本体)的新旧版本进行比较来求取变化元素集合和合理演化路径,然后基于该变化元素集合生成查询测试集,并且借助相应的映射将查询测试集中的查询重写。最后,根据重写后的查询能否返回合法实例,就可以判定查询重写所使用的映射是否失效。     

SPARQL查询的关系代数表示与转换方法

为解决基于本体的数据集成系统中的查询转换问题,提出SPARQL查询的关系代数表示和转换方法。引入RDF图模式的关系代数,定义了五种基本的关系运算,给出了SPARQL查询的关系代数表示;提出了SPARQL到SQL的查询转换方法,将基于本体的SPARQL查询转换为可在关系数据库上直接执行的SQL查询,从而实现关系数据库的集成。系统实现表明,该方法能够有效地实现查询语言的转换。     

基于OWL本体论映射的数据库网格语义模式集成研究

本文提出了一种在数据库网格中OWL本体论映射机制如何用于基于语义的数据库模式集成。方法是首先把关系模式转化为RDF/OWL语义描述以完成局部映射,再通过把局部数据语义与全局共享本体建立联系来完成全局映射。本质是把异构数据库模式的语义通过本体显性地表达出来,并在语义Web层完成模式的集成。特点是实现了在统一的语义层次上进行共享与查询,同时采用了局部映射与全局映射松耦合的构架,其特有的分层结构使得在跨库/单库环境中进行语义查询变得更加灵活。     

Object-oriented query language access to relational databases: A semantic framework for query translation

This research investigates and approach to query processing in a multidatabase system that uses an objectoriented model to capture the semantics of other data models. The object-oriented model is used to construct a global schema, defining an integrated view of the different schemas in the environment. The model is also used as a self-describing model to build a meta-database for storing information about the global schema. A unique aspect of this work is that the object-oriented model is used to describe the different data models of the multidatabase environment, thereby extending the meta database with semantic information about the local schemas. With the global and local schemas all represented in an object-oriented form, structural mappings between the global schema and each local schema are then easily supported. An object algebra then provides a query language for expressing global queries, using the structural mappings to translate object algebra queries into SQL queries over local relational schema. The advantage of using an object algebra is that the object-oriented database can be viewed as a blackboard for temporary storage of local data and for establishing relationships between different databases. The object algebra can be used to directly retrieve temporarily-stored data from the object-oriented database or to transparently retrieve data from local sources using the translation process described in this paper.     

QUERY ROUTING IN A PEER-TO-PEER SEMANTIC LINK NETWORK

A semantic link peer-to-peer (P2P) network specifies and manages semantic relationships between peers' data schemas and can be used as the semantic layer of a scalable Knowledge Grid. The proposed approach consists of an automatic semantic link discovery method, a tool for building and maintaining P2P semantic link networks (P2PSLNs), a semantic-based peer similarity measurement for efficient query routing, and the schema mapping algorithms for query reformulation and heterogeneous data integration. The proposed approach has three important aspects. First, it uses semantic links to enrich the relationships between peers' data schemas. Second, it considers not only nodes but also the XML structure in measuring the similarity between schemas to efficiently and accurately forward queries to relevant peers. Third, it copes with semantic and structural heterogeneity and data inconsistency so that peers can exchange and translate heterogeneous information within a uniform view.     

Semi-automated schema integration with SASMINT

The emergence of increasing number of collaborating organizations has made clear the need for supporting interoperability infrastructures, enabling sharing and exchange of data among organizations. Schema matching and schema integration are the crucial components of the interoperability infrastructures, and their semi-automation to interrelate or integrate heterogeneous and autonomous databases in collaborative networks is desired. The Semi-Automatic Schema Matching and INTegration (SASMINT) System introduced in this paper identifies and resolves several important syntactic, semantic, and structural conflicts among schemas of relational databases to find their likely matches automatically. Furthermore, after getting the user validation on the matched results, it proposes an integrated schema. SASMINT uses a combination of a variety of metrics and algorithms from the Natural Language Processing and Graph Theory domains for its schema matching. For the schema integration, it utilizes a number of derivation rules defined in the scope of the research work explained in this paper. Furthermore, a derivation language called SASMINT Derivation Markup Language (SDML) is defined for capturing and formulating both the results of matching and the integration that can be further used, for example for federated query processing from independent databases. In summary, the paper focuses on addressing: (1) conflicts among schemas that make automatic schema matching and integration difficult, (2) the main components of the SASMINT approach and system, (3) in-depth exploration of SDML, (4) heuristic rules designed and implemented as part of the schema integration component of the SASMINT system, and (5) experimental evaluation of SASMINT.     

基于关系数据库的本体构建方法研究

本体可以很好地解决异构数据库集成中语义异构问题。由关系数据库模式转换到本体结构是信息集成的基础技术之一。该文提出了一种基于关系数据库的本体建模方法,并对构建过程中丢失的外键所包含的语义信息进行恢复,使得数据库的语义表达更为完整。     

A framework for semantic grouping in P2P databases

Sharing of structured data in decentralized environments is a challenging problem, especially in the absence of a global schema. Social network structures map network links to semantic relations between participants in order to assist in efficient resource discovery and information exchange. In this work, we propose a scheme that automates the process of creating schema synopses from semantic clusters of peers which own autonomous relational databases. The resulting mediated schemas can be used as global interfaces for relevant queries. Active nodes are able to initiate the group schema creation process, which produces a mediated schema representative of nodes with similar semantics. Group schemas are then propagated in the overlay and used as a single interface for relevant queries. This increases both the quality and the quantity of the retrieved answers and allows for fast discovery of interest groups by joining peers. As our experimental evaluations show, this method increases both the quality and the quantity of the retrieved answers and allows for faster discovery of semantic groups by joining peers.