fdata-02-00015-g0002_Applying Answer Set Programming for Knowledge-Based Link Prediction on Social Interaction Networks.tif

Link prediction targets the prediction of possible future links in a social network, i. e., we aim to predict the next most likely links of the network given the current state. However, predicting the future solely based on (scarce) historic data is often challenging. In this paper, we investigate, if we can make use of additional (domain) knowledge to tackle this problem. For this purpose, we apply answer set programming (ASP) for formalizing the domain knowledge for social network (and graph) analysis. In particular, we investigate link prediction via ASP based on node proximity and its enhancement with background knowledge, in order to test intuitions that common features, e. g., a common educational background of students, imply common interests. In addition, then the applied ASP formalism enables explanation-aware prediction approaches.

链接预测旨在预测社交网络中可能出现的未来链接，即根据当前网络状态预测最可能的后续链接。然而，仅凭（稀缺的）历史数据预测未来往往颇具挑战。在本研究中，我们探讨是否可以利用额外的（领域）知识来解决这一问题。为此，我们应用答案集编程（ASP）来形式化社交网络（及图）分析中的领域知识。具体而言，我们通过ASP研究基于节点邻近性的链接预测，并探讨其与背景知识的结合，以检验普遍特征，例如学生的共同教育背景暗示着共同的兴趣。此外，所应用的ASP形式化方法还使得预测方法具备了解释意识。