【作者】 李斌；

【导师】 李征；

【作者基本信息】 河南大学 ， 计算机应用技术， 2018， 硕士

【摘要】 随着软件即服务与面向服务架构技术的发展,互联网上的Web服务不断增加,且被广泛应用到软件开发中。面对网络上大量日益增长的Web服务,以及用户的个性化需求,如何帮助用户准确高效地找到其所需的服务是一个关键问题。有研究表明,已有的一些服务发现方法尚未解决用户查询模糊的问题,对用户的功能查询缺乏语义扩展。此外,对Web服务的发现是基于整个语料库中的服务进行匹配,没有对其进行有效的分类组织,导致服务发现的效率不高。针对上述问题,本文主要开展如下研究:(1)基于关联规则和改进K-means算法进行领域本体的构建。该方法首先基于支持向量机进行迭代式服务分类以得到领域词汇排序表,从中选取前h个词汇作为领域概念,然后利用关联规则和权重构建概念向量,使用改进K-means算法对概念向量进行层次聚类,经迭代得到初始领域本体,并采用WordNet对初始本体进行语义丰富,为Web服务发现时用户的查询扩充奠定基础;(2)利用改进相似度计算的谱聚类算法进一步对领域内的服务进行聚类,并挖掘服务所属的主题。该方法基于网络中节点的相似性传播原理,通过设置阈值找到与各个文档相似度较大的文档集合,进而使用Jaccard系数计算两个文档集合间的相似度,根据得到的文档相似度矩阵,借鉴图论的K路划分问题,利用NJW谱聚类算法,对Web服务文档进行聚类,同时实现文档聚类后的主题抽取,目的是降低搜索空间,提高服务发现的效率;(3)基于上述研究内容,根据“用户查询-本体扩充-主题匹配-服务匹配”的策略,进行按需服务发现,并通过ProgrammableWeb上的数据集验证了方法的可行性和有效性,最后利用Java语言实现了基于领域本体的Web服务发现系统。本文提出的基于领域本体和谱聚类的Web服务发现方法,一定程度上能够引导用户明确其需求进而解决用户查询模糊的问题,同时,能够对服务进行有效的组织,降低搜索空间,进而提高Web服务发现的效率。更多还原

【Abstract】 With the development of software as a service and service-oriented architecture technology,the amount of available Web services resources on the Internet is increasing and widely used in software development.However,in the face of a large and growing number of Web services on the Internet,it is still a key problem to help users accurately and efficiently find the services they need.Studies have shown that some existing service discovery methods have not solved the problem of user query ambiguity,and lack of semantic extension to user’s functional queries.In addition,the discovery of Web services is based on matching services in the whole corpus,without effective services classification and organization,resulting in low retrieval efficiency.In response to the above issues,this paper mainly carries out the following research:(1)Domain ontology is constructed based on association rules and improved K-means algorithm.Firstly,based on the domain vocabulary sorting table obtained by the iterative service classification using SVM,the top h words are selected as the domain concepts,then use the association rules and weights to construct the concept vector,and the improved K-means algorithm is used to cluster the concept vector to get the initial domain ontology;Lastly,the ontology is enriched by WordNet,which provides the foundation for the user’s query expansion when the Web service is discovered;(2)Based on improved similarity computation,the spectrum clustering is used to complete the clustering of services in the domain and mining the theme of the service.The method is based on the principle of the similarity propagation of nodes in the network,we find a set of documents that are more similar to each document by setting the threshold,and further calculate the similarity between each two document sets by using Jaccard coefficients,according to the similarity matrix and the reference K partitioning problem in graph theory,we use NJW algorithm to cluster Web documents,and realize the topic extraction after clustering,which is to reduce the search space and improve the efficiency of service discovery.(3)Based on the above research contents,according to the strategy of “user query-ontology expansion-topic matching-service matching”,on-demand services discovery is conducted,and the feasibility and effectiveness of the approach are verified by the dataset on ProgrammableWeb,and the Web services retrieval system based on domain ontology is implemented using Java language.The Web service discovery method based on domain ontology and spectral clustering proposed in this paper can,to a certain extent,guide users t can to clear their needs and solve the problem of ambiguous user queries.At the same time,it can effectively organize services,reduce the search space,and improve the efficiency of Web services discovery.更多还原