Neighbor-Aware Embedding Interaction for Link Prediction

Counterfeit and substandard product crimes pose significant threats to consumer safety and market stability, yet the incompleteness of knowledge graphs in this domain hampers effective case analysis and decision-making. Link prediction (LP) captures both structural and semantic representations of entities to infer latent relationships, thereby mitigating graph incompleteness and enabling more accurate investigation and law enforcement. To achieve this, most existing works employ graph neural networks to aggregate neighbor information of entities, aiming to capture both structural and semantic features in the embedding process. However, due to the heterogeneity of the neighbor information, directly aggregating it can negatively impact the prediction accuracy. In this paper, we propose a neighbor-aware embedding interaction (NEI) model that computes interactions between semantic and structural neighbors to sufficiently aggregate heterogeneous neighbor information, which improves LP performance. Specifically, we first propose dependency direction enhancement module that extracts high-quality semantic neighbors to improve understanding for entities with sparse relationships. Subsequently, a neighbor-aware interaction module captures valuable hidden information from both semantic and structural neighbors, enriching the neighbor embedding representation. Then, we propose a neighbor-aware convolution method to improve the prediction performance by incorporating rich neighbor embedding vectors for head and relation embedding vectors of each triple through interaction. We conducted a series of experiments on three public LP datasets to evaluate the effectiveness of the NEI model. The NEI model outperforms the suboptimal baseline by 7. 3% and 5. 3% in the WN18RR dataset in terms of MRR and Hits@1, respectively.