AUTHOR=Güven Çiçek , Atzmueller Martin 

TITLE=Applying Answer Set Programming for Knowledge-Based Link Prediction on Social Interaction Networks

JOURNAL=Frontiers in Big Data

VOLUME=2

YEAR=2019

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2019.00015

DOI=10.3389/fdata.2019.00015

ISSN=2624-909X

ABSTRACT=<p>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.</p>