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首页> 外文期刊>International Journal of Web Engineering >Web Service Matchmaking Using Web Search Engine and Machine Learning
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Web Service Matchmaking Using Web Search Engine and Machine Learning

机译：使用Web搜索引擎和机器学习进行Web服务配对

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摘要

Web Services discovery that locates adequate services, has been studied very actively for better quality of service retrieval. Starting from conventional keyword matching, logic-based matching and combination of the methods with information retrieval approach have been proposed to enable better discovery performance. The combining method using term-similarity can overcome the decision failure when the keyword or the logic-based methods were applied, and it was shown that the methods outperform the existing methods. And researches to aggregate matchmaking variants by machine learning has been attempted, and it also improves the discovery performance. The approaches still suffer from fixed corpus set for term similarity calculation. In this research, we attempted to calculate the similarity based on search engine to reflect the current Web context. Tokenized terms are used for the matchmaking degree. Variants for the matchmaking from ontology and term similarity are aggregated using Support Vector Machine (SVM) with non-linear kernel function. Matchmaking test on the trip domain service discovery was conducted. Experimental result based on the standard measure of precision and recall rate for the top 1-20 services of matched result on the trip domain test set are shown.
机译：为了更好地检索服务质量，已经非常积极地研究了找到适当服务的Web服务发现。从常规的关键字匹配开始，已经提出了基于逻辑的匹配以及方法与信息检索方法的组合，以实现更好的发现性能。使用关键词相似度的组合方法可以克服关键词或基于逻辑的方法在决策上的失败，并且表明该方法优于现有方法。并且已经尝试了通过机器学习来聚合配对匹配变体的研究，并且它还提高了发现性能。这些方法仍然存在用于术语相似度计算的固定语料库。在这项研究中，我们尝试基于搜索引擎来计算相似度以反映当前的Web上下文。标记词用于匹配度。使用具有非线性核函数的支持向量机（SVM）汇总本体和术语相似性的匹配项。对旅行域服务发现进行了配对测试。显示了基于精度和召回率的标准度量的实验结果，该结果是旅行域测试集中匹配结果的前1-20个服务。

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