Folklore knowledge graph completion by fusing neighborhood information

Folklore knowledge graph (KG), the domain knowledge base, has received a lot of interest and has the potential to be widely employed in context understanding and knowledge service scenarios. However, folk literature works often use short sentences and just elaborate on partial relations between characters and things in detail. The KG does not fully cover all the real domain knowledge, and it is necessary to complete. Existing KG completion (KGC) methods are hard to differentiate various neighborhood information and semantic gaps within relations. This paper proposes the folklore KG completion model FolkKGC by fusing the neighborhood information. First, the relation-aware gate attention mechanism is designed for neighborhood information fusion to effectively represent the folklore representation of entities. The relevant fine-grained folklore representations are then produced by fusing the neighborhood information of relations using a relation learner based on cross-neighborhood similarity. Comparison and ablation experiments are conducted on the datasets extracted from folklore texts. The results show that our method outperforms the state-of-the-art models on MRR and Hits@n, which verifies the effectiveness of our proposed model.