A knowledge graph (KG) represents a domain of interest with a graph where some of the involved entities are linked with an edge. Knowledge Graph Completion (KGC) is a well-known task for KGs which requires finding missing connections. KGC has been studied for many years with multiple solutions available based on both symbolic and sub-symbolic techniques. In this paper, we would like to answer the question: can parameter learning for Probabilistic Logic Programming be a competitive algorithm to solve the KGC task? An empirical evaluation on the most common KGC datasets allows us to provide a negative answer to such a question.

Logic Programming for Knowledge Graph Completion

Azzolini D.
Primo
;
Gentili E.
Penultimo
;
Riguzzi F.
Ultimo
2024

Abstract

A knowledge graph (KG) represents a domain of interest with a graph where some of the involved entities are linked with an edge. Knowledge Graph Completion (KGC) is a well-known task for KGs which requires finding missing connections. KGC has been studied for many years with multiple solutions available based on both symbolic and sub-symbolic techniques. In this paper, we would like to answer the question: can parameter learning for Probabilistic Logic Programming be a competitive algorithm to solve the KGC task? An empirical evaluation on the most common KGC datasets allows us to provide a negative answer to such a question.
2024
Knowledge Graph Completion; Logic Programming; Parameter Learning; Statistical Relational Artificial Intelligence
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2557751
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