Link prediction using a probabilistic description logic

José Eduardo Ochoa Luna, Kate Revoredo, Fabio Gagliardi Cozman

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.
Original languageAmerican English
Pages (from-to)397-409
Number of pages13
JournalJournal of the Brazilian Computer Society
DOIs
StatePublished - 1 Dec 2013
Externally publishedYes

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