Applying ASP for Knowledge-Based Link Prediction With Explanation Generation in Feature-Rich Networks
Çíçek Güven, Dietmar A. Seipel, Martin Atzmueller · IEEE Transactions on Network Science and Engineering · 2020
Link prediction is challenging, especially based on (scarce) historic data or in cold start scenarios. In this paper, we show how to apply answer set programming (ASP) for formalizing link prediction in feature-rich networks, that is - in particular - using domain knowledge for network (and graph) analysis. We show, that applying ASP for link prediction provides a powerful declarative approach, as exemplified using simple predictors, and demonstrate according explanation generation using ASP. We present the application of the proposed methodological approach for explicative link prediction and analysis with explanation generation using different datasets. Keywords. Explainable AI, social network analysis, link prediction, answer set programming, Prolog, domain knowledge