计算机科学 ›› 2021, Vol. 48 ›› Issue (10): 160-166.doi: 10.11896/jsjkx.200900026
于扬1, 邢镔2, 曾骏1, 文俊浩1
YU Yang1, XING Bin2, ZENG Jun1, WEN Jun-hao1
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。
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