Semantic Unlink Prediction in Evolving Social Networks through Probabilistic Description Logic
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Date
2014
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Recently, prediction of new links between two individuals in social networks has gained a lot of attention. However, to fully understand and predict how the network evolves through time, ending relationships also need to be predicted. Although most approaches use graph-based methods for link prediction, these may not be suited for the unlink prediction task. In this paper, we propose an approach for unlink prediction that uses information about the domain of discourse through a probabilistic ontology, specified in the probabilistic description logic CRALC. We empirically evaluated our approach comparing it with standard graph-based and some state of the art unlink methods. The results shows significant improvement on detecting unlinks when considering our proposal. © 2014 IEEE.
Description
Keywords
Data description , Forecasting , Formal languages , Graphic methods , Intelligent systems , Semantics , Social networking (online) , Evolving networks , Graph-based , Graph-based methods , Link prediction , Prediction tasks , Probabilistic descriptions , Probabilistic ontologies , State of the art , Probabilistic logics