WordNet serves as a very essential knowledge source for various downstream Natural Language Processing (NLP) tasks. Since this is a human-curated resource, building such a resource is very cumbersome and time-consuming. Even though for languages like English, the existing WordNet is reasonably rich in terms of coverage, for resource-poor languages like Bengali, the WordNet is far from being reasonably sufficient in terms of coverage of vocabulary and relations between them. In this paper, we investigate the usefulness of some of the existing knowledge graph completion algorithms to enrich Bengali WordNet automatically. We explore three such techniques namely DistMult, ComplEx, and HolE, and analyze their effectiveness for adding more relations between existing nodes in the WordNet. We achieve maximum Hits@1 of 0.412 and Hits@10 of 0.703, which look very promising for low resource languages like Bengali.