A performant and incremental algorithm for knowledge graph entity typing

Knowledge Graph Entity Typing (KGET) is a subtask of knowledge graph completion, which aims at inferring missing entity types by utilizing existing type knowledge and triple knowledge of the knowledge graph. Previous knowledge graph embedding (KGE) algorithms infer entity types through trained entity embeddings. However, for new unseen entities, KGE models encounter obstacles in inferring their types. In addition, it is also difficult for KGE models to improve the performance incrementally with the increase of added data. In this paper, we propose a statistic-based KGET algorithm which aims to take both performance and incrementality into consideration. The algorithm aggregates the neighborhood information and type co-occurrence information of target entities to infer their types. Specifically, we first compute the type probability distribution of the target entity in the semantic context of given fact triple. Then the probability information of fact triples involved in the target entity is aggregated. In addition to local neighborhood information, we also consider capturing global type co-occurrence information for target entities to enhance inference performance. Extensive experiments show that our algorithm outperforms previous statistics-based KGET algorithms and even some KGE models. Finally, we design an incremental inference experiment, which verifies the superiority of our algorithm in predicting the types of new entities, and the experiment also verifies that our algorithm has excellent incremental property.

Automated summary

This paper proposes a statistic-based algorithm for inferring missing entity types in knowledge graph, considering both performance and incrementality. The algorithm aggregates neighborhood information and type co-occurrence information to infer types, outperforming previous statistics-based algorithms and some other models.