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Knowledge graph (KG) embedding is a fundamental task in natural languageprocessing, and various methods have been proposed to explore semantic patternsin distinctive ways. In this paper, we propose to learn an ensemble byleveraging existing methods in a relation-aware manner. However, exploringthese semantics using relation-aware ensemble leads to a much larger searchspace than general ensemble methods. To address this issue, we propose adivide-search-combine algorithm RelEns-DSC that searches the relation-wiseensemble weights independently. This algorithm has the same computation cost asgeneral ensemble methods but with much better performance. Experimental resultson benchmark datasets demonstrate the effectiveness of the proposed method inefficiently searching relation-aware ensemble weights and achievingstate-of-the-art embedding performance. The code is public athttps://github.com/LARS-research/RelEns.

Relation-aware Ensemble Learning for Knowledge Graph Embedding