As web-based social network allows anyone to post the content without any restriction, the trustworthiness of the content creator plays an important role before using the content. An effiective way to find the trustworthiness is, by analyzing the web resources related to the content creator. Therefore the trustworthiness is assessed using the provenance based ontological model called W7 model. Since it is a real time data, the computed trust for each reviewer using the ontological model is uncertain and vague. An appropriate way to classify such data is using the fuzzy logic with gradual trust level. As the computed trust data are feature-based and non-symbolic, the classification ambiguity need to be reduced greatly. This is achieved with the fuzzy decision tree approach, which is a fusion of fuzzy sets with decision tree. The truth of the rule is crucial in trustworthy user classification, as highly truthful rules really increase the credibility of the user in their domain. Therefore, in the proposed model, degree of truth is used as a pruning criteria that classifies the users with minimum number of fuzzy evidence or knowledge. This paper proposes a semantic provenance based gradual trust model to classify the trustworthy reviewers in a book-based social networks using fuzzy decision tree approach. Performance analysis of the proposed model in the terms of classifier accuracy, precision, recall, the number of rules generated and its time complexity are discussed. The analysis shows that the proposed learning model outperforms other classification models. This method is also applied to other data sets and the performance of the classifier is assessed.

中文翻译：

---

使用模糊决策树在基于书籍的社交网络上基于语义来源的可信用户分类

由于基于网络的社交网络允许任何人不受任何限制地发布内容，因此内容创建者的可信度在使用内容之前起着重要作用。找到可信度的一种有效方法是分析与内容创建者相关的网络资源。因此，使用称为 W7 模型的基于起源的本体模型来评估可信度。由于它是实时数据，因此使用本体模型计算的每个评论者的信任度是不确定和模糊的。对此类数据进行分类的适当方法是使用具有逐渐信任级别的模糊逻辑。由于计算的信任数据是基于特征的和非符号的，因此需要大大降低分类歧义。这是通过模糊决策树方法实现的，该方法是模糊集与决策树的融合。规则的真实性对于可信赖的用户分类至关重要，因为高度真实的规则确实提高了用户在其域中的可信度。因此，在所提出的模型中，真实度被用作修剪标准，对具有最少数量的模糊证据或知识的用户进行分类。本文提出了一种基于语义来源的渐进信任模型，使用模糊决策树方法对基于书籍的社交网络中的可信赖评论者进行分类。讨论了该模型在分类器准确率、精度、召回率、生成规则的数量及其时间复杂度方面的性能分析。分析表明，所提出的学习模型优于其他分类模型。该方法也适用于其他数据集，并评估分类器的性能。