METEOR–S WSDI: A Scalable P2P Infrastructure of Registries for Semantic Publication and Discovery of Web Services 阅读分析

这篇论文反映的是佐治亚大学的一个研究小组在语义web组合方面的一个研究成果。这个小组在基于语义的服务发现，组合，调用和性能评估等方面都做了工作，这篇文章主要涉及其服务注册和服务发现方面。其目录如下图所示

 

简单说，这篇文章的主要思想是：把服务的注册（描述）和服务的查找需求（requirements）都跟相关的ontology关联起来。这样，服务查找时，就可以进行语义匹配，查找到合适的服务。同时，所有的registry都按p2p的方式组织起来，进行信息同享和通信，以达到可伸缩的目标。

 

文章中对自己的贡献是这样描述的：

（1） Creating a scalable infrastructure for accessing multiple registries；

（2）Semantically dividing registries into domains using semantics for improved service publication and discovery；（3） Implementing two approaches for annotating service descriptions (WSDL) and an algorithm for semantic publication of Web services in UDDI；

（4）Implementing an algorithm that uses these semantics during service discovery

 

在组织注册机构时，使用了registry ontology，其主要作用是：1）将每一个registry与ontology中的一个node（domain）关联起来，并且一个ontology中的一个node可以关联多个registries。2）在ontology中反映出来的concepts之间的关联，同时也反映了与其关联的registry之间的关系。3）服务查找时，可以根据domain来选择相关的registries，缩小范围。

 

对于服务来说，可以给服务加入语义信息。方式是：映射服务的输入input和输出output到ontology中的concepts中去。

 

文章的重点是基于语义的服务发布和发现。其细节如图所示

 

具体发布和发现细节，文章如下描述：“Figure 7 shows the conceptual process of mapping WSDL concepts to the nodes in a

domain specific ontology during service publication. It also depicts the creation of template using nodes in domain specific ontology for semantic discovery of services. As shown in the figure, the input concept of WSDL file FlightNum is mapped to the flightNo node in the AirTravel ontology. In addition, the output concepts DepartureCity and ArrivalCity are both mapped to the airportCity in the AirTravel ontology. These mappings can be used in the discovery process, by having the user map his discovery requirements to nodes in the domain specific ontology. This can be achieved by creating a template based on the concepts from the domain specific ontology.”

 

解释如下：服务的WSDL文件中有一个input概念FlightNum,被映射为AirTravel ontology中的flightNo节点，同时，两个output概念DepartureCity和ArrivalCity都被映射为AirTravel ontology中的airportCity。类似的事情发生在服务发现中，用户映射自己的服务发现需求到domain specific ontology中的节点中。

 

 

在将WSDL中的input和output映射到ontology中时，有两个方法：人工方法和半自动办法。人工方法很简单，直接给用户提供一个界面，左边是ontology，右边是WSDL中的conpects。用户直接用鼠标点击完成左右概念之间的关联。半自动方法就要复杂一些，主要包括直接概念之间的比较，和WSDL中概念与ontology树中某概念以下各层概念之间的相似度比较。具体算法文章中描述如下：“The SAWS algorithm compares a concept from WSDL and an ontological concept and returns the degree of similarity (DS) between them. It is a combination of a structure matching algorithm and an element level matching algorithm. The element level matching algorithm calculates the linguistic similarity between the concepts whereas the structure matching algorithm considers the similarity between sub-trees of those concepts and calculates the structural similarity. The overall DS is then calculated as the geometric mean of the structural similarity and linguistic similarity of these two concepts. The degree of similarity is assessed on a scale of 0 to 1. Based on the degree of similarity, the user can accept or reject the mappings.”