Multinomial Naive Bayes Categorization for Semantic Web Services

Naoufal El Allali, Mourad Fariss, H. Asaidi, Mohamed Bellouki
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Abstract

The significant existence of web services is challenging to the researchers regarding their diversity of types and their diffusion. It may lead to difficulty in identifying the relevant service during the discovery or composition process. To tackle this problem, we propose a new method to categorize semantic web services based on the Naive Bayes algorithm using a weighting method (TF-IDF), which binds a service according to its description importance offered by the service provider to be categorized in a relevant class. It enhances the performance by proposing a compatible combination of the preprocessing techniques (Natural language processing) to achieve a better classification result. This method has been tested on the OWLS-TC dataset, categorized into seven classes, and its accuracy is 93%.
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语义Web服务的多项朴素贝叶斯分类
web服务的显著存在对研究人员提出了挑战,因为它们的类型多样性和扩散性。它可能导致在发现或组合过程中难以识别相关服务。为了解决这一问题,我们提出了一种基于朴素贝叶斯算法的语义web服务分类新方法,该方法使用加权方法(TF-IDF),根据服务提供者提供的描述重要性将服务绑定到相关类中。它通过提出一种兼容的预处理技术(自然语言处理)组合来提高性能,以获得更好的分类结果。该方法在OWLS-TC数据集上进行了测试,分为7类,准确率为93%。
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