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.