Incorporating global–local neighbors with Gaussian mixture embedding for few-shot knowledge graph completion

Few-shot knowledge graph completion (FKGC) aims to predict the missing parts of the query triplet based on a small number of known samples. To solve the above task, many existing approaches enhance entity embedding by encoding local neighbor information and obtain few-shot relational representations by encoding support triples. Although these previous studies have achieved promising results, they still suffer from the following two challenges: (1) Remote neighbor contains rich semantic information, how to effectively encode remote neighbor information is the first challenge? (2) Low-frequency relations and complex relations in the knowledge graph lead to uncertainty in the semantics of the relation, how to effectively model the uncertainty of the few-shot relation is the second challenge? For the former issue, we propose a global–local neighbor encoding module, where global encoder captures remote neighbor features based on relation paths and local encoder uses the task-aware attention mechanism to capture local neighbor features. For the latter issue, we employee the adaptive gaussian mixture model to model few-shot relation, which can adapt to different queries by dynamically adjusting component weights. Link prediction experiments are conducted on two benchmark datasets NELL-One and Wiki-One, and the proposed model achieved 14.0% and 7.8% improvement in the evaluation metric Hits@1 respectively, compared to the strong baseline model FAAN.