TY - JOUR
T1 - Link prediction using a probabilistic description logic
AU - Luna, José Eduardo Ochoa
AU - Revoredo, Kate
AU - Cozman, Fabio Gagliardi
PY - 2013/12/1
Y1 - 2013/12/1
N2 - 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 cr ALL). 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. © 2013 The Brazilian Computer Society.
AB - 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 cr ALL). 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. © 2013 The Brazilian Computer Society.
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U2 - 10.1007/s13173-013-0108-8
DO - 10.1007/s13173-013-0108-8
M3 - Article
SP - 397
EP - 409
JO - Journal of the Brazilian Computer Society
JF - Journal of the Brazilian Computer Society
SN - 0104-6500
ER -