Link prediction using a probabilistic description logic

Due to the growing interest in social networks, link prediction has received significant attention. Link prediction is mostly based on graph-based features, with some recent approaches focusing on domain semantics. We propose algorithms for link prediction that use a probabilistic ontology to enhance the analysis of the domain and the unavoidable uncertainty in the task (the ontology is specified in the probabilistic description logic crALC). The scalability of the approach is investigated, through a combination of semantic assumptions and graph-based features. We evaluate empirically our proposal, and compare it with standard solutions in the literature.

The third author is partially supported by CNPq. The work reported here has received substantial support by FAPESP Grant 2008/03995-5 and FAPERJ Grant E-26/111484/2010. Thanks to Jesus Pascual Mena Chalco for providing us datasets and figures of the Lattes research areas.