Clustering web services is an effective method to solving service computing problems. The key insight behind it is to extract the vectors based on the service description documents. However, the brevity of natural language service description documents typically complicates the vector construction process. To circumvent the difficulty, we propose a novel web service clustering method to vectorize documents based on the semantic similarity, which can be calculated via WordNet and multidimensional scaling (WMS) analysis. We utilize the dataset from the ProgrammableWeb to conduct extensive experiments and achieve prominent advances in precision, recall, and F-measure.

中文翻译：

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基于语义相似度和多维尺度分析的Web服务聚类方法

群集Web服务是解决服务计算问题的有效方法。其背后的关键见解是根据服务描述文档提取向量。但是，自然语言服务描述文档的简短性通常会使向量的构建过程复杂化。为了解决这一难题，我们提出了一种新颖的Web服务聚类方法，该方法可基于语义相似度对文档进行矢量化处理，该方法可通过WordNet和多维缩放（WMS）分析来计算。我们利用来自ProgrammableWeb的数据集进行广泛的实验，并在精度，召回率和F量度方面取得了显着进步。