Ensembling Textual and Structure-Based Models for Knowledge Graph
  Completion
Ananjan Nandi Navdeep Kaur Parag Singla Mausam
We consider two popular approaches to Knowledge Graph Completion (KGC):
textual models that rely on textual entity descriptions, and structure-based
models that exploit the connectivity structure of the Knowledge Graph (KG).
Preliminary experiments show that these approaches have complementary
strengths: structure-based models perform well when the gold answer is easily
reachable from the query head in the KG, while textual models exploit
descriptions to give good performance even when the gold answer is not
reachable. In response, we explore ensembling as a way of combining the best of
both approaches. We propose a novel method for learning query-dependent
ensemble weights by using the distributions of scores assigned by individual
models to all candidate entities. Our ensemble baseline achieves
state-of-the-art results on three standard KGC datasets, with up to 6.8 pt MRR
and 8.3 pt Hits@1 gains over best individual models.