fdata-02-00015-g0001_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，我们旨在根据当前网络状态预测最有可能出现的链接。然而，仅基于（稀缺的）历史数据预测未来往往具有挑战性。在本文中，我们探讨是否能够利用额外的（领域）知识来解决这一问题。为此，我们应用答案集编程（ASP）来形式化领域知识，以用于社交网络（及图）分析。特别是，我们通过ASP研究基于节点邻近度的链接预测及其与背景知识的增强，以检验直觉，即常见的特征，例如，学生共同的学术背景，暗示着共同的兴趣。此外，所应用的ASP形式化还使得具有解释意识的预测方法成为可能。