Knowledge Graphs have gained popularity in the last decade, given their ability to represent huge structured knowledge bases. However, they are often incomplete and thus Knowledge Graph Completion (KGC) is currently a hot topic. In this paper we present our idea of performing KGC by learning liftable probabilistic logic programs via regularization, using LIFTCOVER+, with the aim of obtaining more accurate results while learning a smaller set of rules.
Knowledge Graph Completion with Probabilistic Logic Programming
Gentili E.
2024
Abstract
Knowledge Graphs have gained popularity in the last decade, given their ability to represent huge structured knowledge bases. However, they are often incomplete and thus Knowledge Graph Completion (KGC) is currently a hot topic. In this paper we present our idea of performing KGC by learning liftable probabilistic logic programs via regularization, using LIFTCOVER+, with the aim of obtaining more accurate results while learning a smaller set of rules.File in questo prodotto:
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