Web Service registries have progressively evolved to social networks-like software repositories. Users cooperate to produce an ever-growing, rich source of Web APIs upon which new value-added Web applications can be built. Such users often interact in order to follow, comment on, consume and compose services published by other users. In this context, Web Service discovery is a core functionality of modern registries as needed Web Services must be discovered before being consumed or composed. Many efforts to provide effective keyword-based service discovery mechanisms are based on Information Retrieval techniques as services are described using structured or unstructured textdocuments that specify the provided functionality. However, traditional techniques suffer from term-mismatch, which means that only the terms that are contained in both user queries and descriptions are exploited to perform service retrieval. Early feature learning techniques such as LSA or LDA tried to solve this problem by finding hidden or latent features in text documents. Recently, alternative feature learning based techniques such as Word Embeddings achieved state of the art results for Web Service discovery. In this paper, we propose to learn features from service descriptions by using Variational Autoencoders, a special kind of autoencoder which restricts the encoded representation to model latent variables. Autoencoders in turn are deep neural networks used for unsupervised learning of efficient codings. We train our autoencoder using a real 17 113-service dataset extracted from the ProgrammableWeb.com API social repository. We measure discovery efficacy by using both Recall and Precision metrics, achieving significant gains compared to both Word Embeddings and classic latent features modelling techniques. Also, performance-oriented experiments show that the proposed approach can be readily exploited in practice.

Web服务注册表已逐渐发展为类似社交网络的软件存储库。用户合作以产生不断增长的丰富Web API源，可以在其上构建新的增值Web应用程序。此类用户经常进行交互，以便关注，评论，使用和撰写其他用户发布的服务。在这种情况下，Web服务发现是现代注册表的核心功能，因为必须在使用或组合Web服务之前先发现它们。提供有效的基于关键字的服务发现机制的许多努力都是基于信息检索技术的，因为服务是使用结构化或非结构化文本描述的指定提供的功能的文档。但是，传统技术存在术语不匹配的问题，这意味着仅利用用户查询和描述中包含的术语来执行服务检索。早期的特征学习技术（例如LSA或LDA）试图通过在文本文档中查找隐藏或潜在特征来解决此问题。近来，基于替代特征学习的技术（如单词嵌入）实现了Web服务发现的最新结果。在本文中，我们建议使用变种自动编码器从服务描述中学习功能，这是一种特殊的自动编码器，它将编码表示形式限制为模型潜在变量。反过来，自动编码器是用于有效编码的无监督学习的深度神经网络。我们使用从ProgrammableWeb.com API社交存储库中提取的真实的17113服务数据集来训练我们的自动编码器。我们通过同时使用召回率和精确度指标来衡量发现效果，与词嵌入和经典潜在特征建模技术相比，它们均获得了可观的收益。同样，面向性能的实验表明，所提出的方法可以在实践中轻松利用。