AttIN: Paying More Attention to Neighborhood Information for Entity Typing in Knowledge Graphs

Entity types in knowledge graph (KG) have been employed extensively in downstream tasks of natural language processing (NLP). Currently, knowledge graph entity typing is usually inferred by embeddings, but a single embedding approach ignores interactions between neighbor entities and relations. In this paper, we propose an AttIN model that pays more attention to entity neighborhood information. More specifically, AttIN contains three independent inference modules, including a BERT module that uses the target entity neighbor to infer the entity type individually, a context transformer that aggregates information based on different contributions from the neighbor, and an interaction information aggregator (IIAgg) module that aggregates the entity neighborhood information into a long sequence. In addition, we use exponentially weighted pooling to process these predictions. Experiments on the FB15kET and YAGO43kET datasets show that AttIN outperforms existing competitive baselines while it does not need extra semantic information in the sparse knowledge graph.