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首页> 外文会议>2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing >A neural model for semantically enhancing Web APIs
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A neural model for semantically enhancing Web APIs

机译：用于语义增强Web API的神经模型

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The paper describes an unsupervised neural model for classifying the methods of Web APIs into a large number of classes specified by a domain ontology. As a result of the classification, each method of a Web service is associated to one ontology concept, the name of the concept being further used to semantically annotate the method. The ontology concepts define some functionalities to be offered by different API methods. The names of these concepts are linguistically denoted by verbs or verb phrases that define the action performed by a method. The framework is based on a model of hierarchical self-organizing maps. The methods of the web APIs are encoded in a bag-of-words style, by counting the words that occur in their javadoc documentation. We experimented this automatic semantic annotation model with a data set consisting of APIs of RDF storage tools. The ontology and the APIs to be classified in our experiments are collected from this dataset.
机译：本文描述了一种无监督的神经模型，用于将Web API的方法分类为领域本体指定的大量类。作为分类的结果，Web服务的每种方法都与一个本体概念相关联，该概念的名称进一步用于语义上对该方法进行注释。本体概念定义了一些由不同的API方法提供的功能。这些概念的名称在语言上由动词或动词短语表示，这些动词或动词短语定义了方法执行的动作。该框架基于分层自组织图的模型。通过对Java API文档中出现的单词进行计数，可以将Web API的方法编码为单词袋样式。我们使用由RDF存储工具的API组成的数据集对这种自动语义注释模型进行了实验。从该数据集中收集了我们实验中要分类的本体和API。

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