Mining Trust Relationships from Online Social Networks

• Published in: Journal of computer science and technology 2012, Vol.27 (3), p.492-505
• Main Author: 张宇 丁彤
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial Intelligence; Categories; Computer Science; Data communication; Data mining; Data Structures and Information Theory; Human; Information Systems Applications (incl.Internet); Knowledge; Mathematical analysis; Mining; On-line systems; Online; Ontology; Product reviews; Reasoning; Regular Paper; Resource Description Framework-RDF; Roles; Semantic web; Semantics; Social networks; Software Engineering; Theory of Computation; User generated content; Virtual communities; Web Ontology Language-OWL; Web技术; 信任关系; 在线; 应用程序; 推理机制; 社交; 网络挖掘; 路径表达式
• ISSN: 1000-9000; 1860-4749
• DOI: 10.1007/s11390-012-1238-8

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.