面向在线社会网络的信任关系挖掘
Mining Trust Relationships from Online Social Networks
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摘要: 随着在线社会网络的日益普及,信任在人们相互联系的过程中起着越来越重要的作用.我们依靠个人信任来接受推荐、进行购物决策和选择在线社区中的合作伙伴.因此,如何从在线社会网络中挖掘出信任关系成为一个重要的研究课题.现有的信任挖掘方法存在以下一些缺点:首先,信任是领域相关的,然而绝大多数方法忽略了信任关系的领域属性,从而导致信任计算的准确率低.其次,由于在线社会网络中的数据不能直接被机器所理解和处理,传统的挖掘方法需要较多的人工干预而且很难被推广到其他应用程序中进行使用.为了解决上述问题,我们提出了一个基于语义的信任推理机制从在线社会网络中自动地挖掘信任关系.我们强调成对关系的领域属性,并且利用语义网技术构建了一个领域本体从而实现数据交互和知识共享.我们采用基于角色和基于行为的推理功能推理出隐式信任关系和领域相关的信任关系.我们利用路径表达式来扩展推理规则从而使得信任挖掘无需过多人工干预而能够自动地进行处理.我们从Epinions网站上抽取现实生活中的真实数据进行实验,实验结果验证了我们所提出方法的有效性和及其所具有的广泛应用价值.Abstract: With the growing popularity of online social network, trust plays a more and more important role in connecting people to each other. We rely on our personal trust to accept recommendations, to make purchase decisions and to select transaction partners in the online community. Therefore, how to obtain trust relationships through mining online social networks becomes an important research topic. There are several shortcomings of existing trust mining methods. First, trust is category-dependent. However, most of the methods overlook the category attribute of trust relationships, which leads to low accuracy in trust calculation. Second, since the data in online social networks cannot be understood and processed by machines directly, traditional mining methods require much human effort and are not easily applied to other applications. To solve the above problems, we propose a semantic-based trust reasoning mechanism to mine trust relationships from online social networks automatically. We emphasize the category attribute of pairwise relationships and utilize Semantic Web technologies to build a domain ontology for data communication and knowledge sharing. We exploit role-based and behavior-based reasoning functions to infer implicit trust relationships and category-specific trust relationships. We make use of path expressions to extend reasoning rules so that the mining process can be done directly without much human effort. We perform experiments on real-life data extracted from Epinions. The experimental results verify the effectiveness and wide application use of our proposed method.