TY - JOUR
T1 - Applying ASP for Knowledge-Based Link Prediction with Explanation Generationin Feature-Rich Networks
AU - Güven, Çiçek
AU - Atzmueller, Martin
AU - Seipel, Dietmar
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
U2 - 10.1109/TNSE.2020.3047580
DO - 10.1109/TNSE.2020.3047580
M3 - Article
VL - 8
SP - 1305
EP - 1315
JO - Transactions on Network Science and Engineering
JF - Transactions on Network Science and Engineering
IS - 2
ER -