Data-Sharing P2P Networks with Semantic Approximation Capabilities

The synergy between peer-to-peer systems and semantic Web technologies supports large-scale sharing of semantically rich data, usually represented through schemas such as RDF. Peers rarely share the same vocabulary, so the resulting heterogeneity of data representations introduces new challenges for the efficient and effective retrieval of relevant information. The authors leverage the presence of semantic approximations between peers' schemas to improve query routing by identifying the peers that best satisfy the user's requests, and to inform users of the relevance of the returned answers through a ranking mechanism that promotes the most semantically related results.     

一种基于对等语义连接网络的路由查询

对等网络（简称P2P）的非集中结构、良好的自治性及容错性等特征,使其成为Internet上有效的信息共享模型。提出一种非结构化对等语义连接网络（NSPSLN）指定和管理在节点数据框架之间的语义关系,从而实现一个基于节点类似于有效路由查询的测量尺度,再形成和不同种类数据的综合模式映射算法。通过研究,为分布式资源提供一种新的方法,并加速知识产生在合作的研究过程中的繁殖、熔化和管理的过程。     

Schema mapping and query translation in heterogeneous P2P XML databases

Peers in a peer-to-peer data management system often have heterogeneous schemas and no mediated global schema. To translate queries across peers, we assume each peer provides correspondences between its schema and a small number of other peer schemas. We focus on query reformulation in the presence of heterogeneous XML schemas, including data–metadata conflicts. We develop an algorithm for inferring precise mapping rules from informal schema correspondences. We define the semantics of query answering in this setting and develop query translation algorithm. Our translation handles an expressive fragment of XQuery and works both along and against the direction of mapping rules. We describe the HePToX heterogeneous P2P XML data management system which incorporates our results. We report the results of extensive experiments on HePToX on both synthetic and real datasets. We demonstrate our system utility and scalability on different P2P distributions.     

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。     

XML application schema matching using similarity measure and relaxation labeling

Information integration for distributed and heterogeneous data sources is still an open challenging, and schema matching is critical in this process. This paper presents an approach to automatic elements matching between XML application schemas using similarity measure and relaxation labeling. The semantic modeling of XML application schema has also been presented. The similarity measure method considers element categories and their properties. In an effort to achieve an optimal matching, contextual constraints are used in the relaxation labeling method. Based on the semantic modeling of XML application schemas, the compatible constraint coefficients are devised in terms of the structures and semantic relationships as defined in the semantic model. To examine the effectiveness of the proposed methods, an algorithm for XML schema matching has been developed, and corresponding computational experiments show that the proposed approach has a high degree of accuracy.     

The Piazza peer data management system

Intuitively, data management and data integration tools are well-suited for exchanging information in a semantically meaningful way. Unfortunately, they suffer from two significant problems: They typically require a comprehensive schema design before they can be used to store or share information and they are difficult to extend because schema evolution is heavyweight and may break backward compatibility. As a result, many small-scale data sharing tasks are more easily facilitated by nondatabase-oriented tools that have little support for semantics. The goal of the peer data management system (PDMS) is to address this need: We propose the use of a decentralized, easily extensible data management architecture in which any user can contribute new data, schema information, or even mappings between other peers' schemes. PDMSs represent a natural step beyond data integration systems, replacing their single logical schema with an interlinked collection of semantic mappings between peers' individual schemas. This paper describes-several aspects of the Piazza PDMS, including the schema mediation formalism, query answering and optimization algorithms, and the relevance of PDMSs to the semantic Web.     

自治异构数据源聚集模型与算法研究

自治异构数据源信息共享的主要问题是如何在P2P环境下对自治数据节点的信息进行统一访问.采用分层结构组织数据源节点能够提高查询效率,减小计算开销,但需要节点根据彼此相似度实现局部的聚类.给出了数据源节点信息发布的形式化描述,提出了基于模式元素匹配的自治异构数据源多重聚集模型以及聚类组织构建过程,采用TA算法解决top-K聚类节点搜索问题,并在此基础上提出TAL算法.实验结果表明,TA和TAL算法能够高效地解决节点聚类排序的问题,特别是TAL算法在聚类节点范围较大时计算性能优于TA.     

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.     

Organisation and management of shared documents in super-peer networks based semantic hierarchical cluster trees

A super-peer semantic network defines the semantic groups that client peers and their super peers use to connect with each other according to the semantic similarity of shared documents in peers. This will result in a query that can be evaluated locally thus reducing query process time and the amount of communication required to obtain related data. In this study, new algorithms are proposed to arrange shared documents in peers into hierarchical cluster tree structures, and links will be set up to connect semantically related super peers. During query evaluation, only related peers will be searched thus the complexity of computing is significantly reduced. Consideration is also given to automatically deciding the threshold for clustering and the construction and other essential operations for hierarchical cluster trees. The experiment results illustrate the features of the approach developed in this study.     

Mapping the semantics of Web text and links

Search engines use content and links to search, rank, cluster, and classify Web pages. These information discovery applications use similarity measures derived from this data to estimate relatedness between pages. However, little research exists on the relationships between similarity measures or between such measures and semantic similarity. The author analyzes and visualizes similarity relationships in massive Web data sets to identify how to integrate content and link analysis for approximating relevance. He uses human-generated metadata from Web directories to estimate semantic similarity and semantic maps to visualize relationships between content and link cues and what these cues suggest about page meaning. Highly heterogeneous topical maps point to a critical dependence on search context.