In this paper, we study the problem of domain-specific related entity finding on highly-heterogeneous knowledge graphs where the task is to find related entities with respect to a query entity. As we are operating in the context of knowledge graphs, our solutions will need to be able to deal with heterogeneous data with multiple objects and a high number of relationship types, and be able to leverage direct and indirect connections between entities. We propose two novel graph- based related entity finding methods: one based on learning to rank and the other based on subgraph propagation in a Bayesian framework. We perform contrastive experiments with a publicly available knowledge graph and show that both our proposed models manage to outperform a strong baseline based on supervised random walks. We also investigate the results of our proposed methods and find that they improve different types of query entities.

[bibtex key=ASONAM:2018:Reinanda]