Relation-aware Ensemble Learning for Knowledge Graph Embedding
This work addresses a specific bottleneck in knowledge graph embedding for NLP applications, offering an incremental improvement over existing ensemble methods.
The paper tackles the problem of efficiently searching relation-aware ensemble weights for knowledge graph embedding by proposing a divide-search-combine algorithm, achieving state-of-the-art performance on benchmark datasets.
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. To address this issue, we propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently. This algorithm has the same computation cost as general ensemble methods but with much better performance. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in efficiently searching relation-aware ensemble weights and achieving state-of-the-art embedding performance. The code is public at https://github.com/LARS-research/RelEns.