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.

Knowledge Graph Entity Typing (KGET) 是知识图补全的子任务，旨在利用现有类型知识和三元组知识推断缺失的实体类型的知识图谱。先前的知识图嵌入 (KGE) 算法通过经过训练的实体嵌入来推断实体类型。然而，对于新的看不见的实体，KGE 模型在推断它们的类型时遇到障碍。此外，KGE 模型也很难随着添加数据的增加而逐步提高性能。在本文中，我们提出了一种基于统计的 KGET 算法，旨在同时考虑性能和增量。该算法聚合目标实体的邻域信息和类型共现信息来推断它们的类型。具体来说，我们首先计算目标实体在给定事实三元组的语义上下文中的类型概率分布。然后聚合目标实体涉及的事实三元组的概率信息。除了局部邻域信息外，我们还考虑捕获目标实体的全局类型共现信息以增强推理性能。大量实验表明，我们的算法优于以前基于统计的 KGET 算法，甚至优于某些 KGE 模型。最后，我们设计了一个增量推理实验，验证了我们的算法在预测新实体类型方面的优越性，实验也验证了我们的算法具有优良的增量性。