  Knowledge graph (KG) link prediction is a fundamental task in artificial
intelligence, with applications in natural language processing, information
retrieval, and biomedicine. Recently, promising results have been achieved by
leveraging cross-modal information in KGs, using ensembles that combine
knowledge graph embeddings (KGEs) and contextual language models (LMs).
However, existing ensembles are either (1) not consistently effective in terms
of ranking accuracy gains or (2) impractically inefficient on larger datasets
due to the combinatorial explosion problem of pairwise ranking with deep
language models. In this paper, we propose a novel tiered ranking architecture
CascadER to maintain the ranking accuracy of full ensembling while improving
efficiency considerably. CascadER uses LMs to rerank the outputs of more
efficient base KGEs, relying on an adaptive subset selection scheme aimed at
invoking the LMs minimally while maximizing accuracy gain over the KGE.
Extensive experiments demonstrate that CascadER improves MRR by up to 9 points
over KGE baselines, setting new state-of-the-art performance on four benchmarks
while improving efficiency by one or more orders of magnitude over competitive
cross-modal baselines. Our empirical analyses reveal that diversity of models
across modalities and preservation of individual models' confidence signals
help explain the effectiveness of CascadER, and suggest promising directions
for cross-modal cascaded architectures. Code and pretrained models are
available at https://github.com/tsafavi/cascader.
