BCRL: Long Text Friendly Knowledge Graph Representation Learning

Gang Wu, Wenfang Wu, Leilei Li, Guodong Zhao, Donghong Han, Baiyou Qiao

Abstract: The sparse data and large computational overhead in the use of large-scale knowledge graphs have caused widespread attention to Knowledge Representation Learning (KRL) technology. Although many KRL models have been proposed to embed structure information, their ability to accurately represent newly added entities or entities with few relations is significantly insufficient. In some studies, the introduction of textual information has partially solved this problem. However, most existing text-enhanced models only consider the shallow description information of the entities, and ignore the relation mention information between entities, and deep semantic information between sentences and words, which is not optimized for long texts supplementary information like Wikipedia.