This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph. We propose HittER, a Hierarchical Transformer model to jointly learn Entity-relation composition and Relational contextualization based on a source entity's neighborhood. Our proposed model consists of two different Transformer blocks: the bottom block extracts features of each entity-relation pair in the local neighborhood of the source entity and the top block aggregates the relational information from outputs of the bottom block. We further design a masked entity prediction task to balance information from the relational context and the source entity itself. Experimental results show that HittER achieves new state-of-the-art results on multiple link prediction datasets. We additionally propose a simple approach to integrate HittER into BERT and demonstrate its effectiveness on two Freebase factoid question answering datasets.
@article{arxiv.2008.12813,
title = {HittER: Hierarchical Transformers for Knowledge Graph Embeddings},
author = {Sanxing Chen and Xiaodong Liu and Jianfeng Gao and Jian Jiao and Ruofei Zhang and Yangfeng Ji},
journal= {arXiv preprint arXiv:2008.12813},
year = {2021}
}