Inductive reasoning on knowledge graphs (KG) is taking the role of a converter from a link prediction to a graph classification problem. These conversion tasks use subgraph extraction techniques with graphs neural networks (GNNs) that result in a degradation of GNN expressiveness due to oversmoothing. Recent work has reformulated the subgraph extraction task in this problem as a local clustering procedure based on a personalized PageRank. However, despite obtaining better results, this approach is affected by the density of the network, limiting its results to networks with medium-low density. In this paper, we propose a new subgraph extraction method that takes into account network density using a kinship-based approach. In the evaluation, using real KGs, the proposed algorithm considerably improves the link prediction model in dense networks.