Negative Sample Generation for Geographic Knowledge Graph Embedding via Joint Entity Semantic Similarity and Clustering

ABSTRACT Geographic knowledge graph (GKG) embedding (referred to as knowledge representation learning) enables the mapping of geographic entities and geographic relationships into a continuous vector space, thereby better capturing the semantic and structural information among entities in geographic space. The representation learning of GKGs requires generating corresponding negative samples based on positive samples. Negative sampling is an essential component of GKG embedding models. However, traditional negative sample generation algorithms suffer from high error rates and poor adaptability to GKGs, leading to potential replacements of entities that may not be logically reasonable in terms of geographic relationships, thus reducing the model's performance and generalization ability. To address the aforementioned challenge, this paper proposes a novel negative sampling approach for GKGs embedding, predicated on the amalgamation of entity semantic congruence and clustering techniques. Primarily, the proposed methodology employs the FastText model to encode entities within the KG into vector representations, thereby aiming to procure precise semantic embeddings of said entities. Subsequently, for improved delineation of semantically akin entities, the K‐means algorithm is invoked to partition the entities into distinct clusters. Within the purview of negative sampling, entities within the same cluster as the substituted entity are randomly selected as negative instances, thus augmenting the fidelity of negative sample generation. Finally, experiments and analyses were conducted on the open‐source datasets WN18, FB15k, and the constructed geographic dataset GKG12. The experimental results show that the proposed method can effectively improve the accuracy of GKG representation learning compared to the existing representation models.