Entity type inference based on path walking and inter-types relationships

As a crucial task for knowledge graphs (KGs), knowledge graph entity type inference (KGET) has garnered increasing attention in recent years. However, recent methods overlook the long-distance information pertaining to entities and the inter-types relationships. The neglect of long-distance information results in the omission of crucial entity relationships and neighbors, consequently leading to the loss of path information associated with missing types. To address this, a path-walking strategy is utilized to identify two-hop triplet paths of the crucial entity for encoding long-distance entity information. Moreover, the absence of inter-types relationships can lead to the loss of the neighborhood information of types, such as co-occurrence information. To ensure a comprehensive understanding of inter-types relationships, we consider interactions not only with the types of single entity but also with different types of entities. Finally, in order to comprehensively represent entities for missing types, considering both the dimensions of path information and neighborhood information, we propose an entity type inference model based on path walking and inter-types relationships, denoted as "ET-PT". This model effectively extracts comprehensive entity information, thereby obtaining the most complete semantic representation of entities. The experimental results on publicly available datasets demonstrate that the proposed method outperforms state-of-the-art approaches.