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.

• R. Reinanda, E. Meij, J. Pantony, and D. Jonathan, “Related entity finding on highly-heterogeneous knowledge graphs,” in Asonam, 2018.
  [Bibtex]

  @inproceedings{ASONAM:2018:Reinanda,
  Author = {Reinanda, Ridho and Meij, Edgar and Pantony, Joshua and Dorando Jonathan},
  Booktitle = {ASONAM},
  Date-Added = {2018-09-27 21:43:39 +0100},
  Date-Modified = {2018-09-27 21:55:03 +0100},
  Series = {{ASONAM} '18},
  Title = {Related Entity Finding on Highly-heterogeneous Knowledge Graphs},
  Year = {2018},
  Bdsk-Url-1 = {http://doi.acm.org/10.1145/3209978.3210031},
  Bdsk-Url-2 = {https://doi.org/10.1145/3209978.3210031}}