KSN:一种基于知识图谱和相似度网络的Web服务发现模型  

KSN:A Web Service Discovery Method Based on Knowledge Graph and Similarity Network

作　　者：于扬 邢镔 曾骏[1] 文俊浩[1] YU Yang;XING Bin;ZENG Jun;WEN Jun-hao(School of Big Data&Software Engineering,Chongqing University,Chongqing 400000,China;Chongqing Innovation Center of Industrial Big-Data Co.Ltd,National Engineering Laboratory for Industrial Big-Data Application Technology,Chongqing 400000,China)

机构地区：[1]重庆大学大数据与软件学院,重庆400000 [2]重庆工业大数据创新中心有限公司应用技术国家工程实验室,重庆400000

出　　处：《计算机科学》2021年第10期160-166,共7页Computer Science

基　　金：国家重点研发计划课题(2019YFB1706104)。

摘　　要：服务发现旨在解决服务信息爆炸的问题,找到定位满足服务请求者需求的服务。由于服务描述信息主要由带有噪声的短文本组成,并且具有语义稀疏的特征,因此很难提取服务描述文档的隐含上下文信息,此外,传统的服务发现方法在获取服务的特征表示后,直接进行相似度计算,其使用的度量函数是不符合人类感知的。针对上述两个问题,文中提出了一种基于知识图谱和神经相似网络的服务发现框架(KSN)。它使用知识图谱来连接服务描述和规格中的实体以获得丰富的外部信息,从而增强服务描述的语义信息,使用卷积神经网络(Convolutional Neural Network,CNN)提取服务的特征向量,并将其作为神经相似网络的输入,神经相似网络会学习一个相似度函数,用于计算服务和请求之间的相似度以支持服务发现过程。通过对ProgrammableWeb爬取的真实服务数据集的大量实验结果表明,就多种评估指标而言,KSN优于现有的Web服务发现方法。Service discovery aims to solve the problem of service information explosion,find and locate services that meet the needs of service requesters.Since the service description information is mainly composed of short text with noise and has the feature of sparse semantics,it is difficult to extract the implicit context information of the service description document.In addition,the traditional service discovery method directly obtains the characteristic representation of the service.According to the cosine similarity to calculate the similarity,the used measurement function is not in line with human perception.In response to the above two problems,this paper proposes a service discovery framework(KSN)based on knowledge graphs and neural similar networks.It uses the knowledge graph to connect the entities in the service description and specifications to obtain rich external information,thereby enhancing the semantic information of the service description.And it uses convolutional neural network(CNN)to extract the feature vector of the service as the input of the neural similarity network.The neural similarity network will learn a similarity function to calculate the similarity between the service and the request to support the service discovery process.A large number of experiments on real service data sets crawled by ProgrammableWeb show that KSN is superior to existing Web service discovery methods in terms of multiple evaluation metrics.

关 键 词：WEB服务发现 服务嵌入 卷积神经网络 知识图谱 神经相似网络 

分 类 号：TP319[自动化与计算机技术—计算机软件与理论]