Please use this identifier to cite or link to this item:
|Title:||Semantic Unlink Prediction in Evolving Social Networks through Probabilistic Description Logic|
|Authors:||Armada de Oliveira, Marcius|
Cerqueira Revoredo, Kate
Ochoa Luna, José Eduardo
|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|
|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.|
|Appears in Collections:||Artículos de investigación|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.