Geography-Enhanced Link Prediction Framework for Knowledge Graph Completion

Knowledge graphs contain knowledge about the world and provide a structured representation of this knowledge. Current knowledge graphs contain only a small subset of what is true in the world. Link prediction approaches aim at predicting new links for a knowledge graph given the existing links among the entities. Recent years have witnessed great advance of representation learning (RL) based link prediction models, which represent entities and relations as elements of a continuous vector space. However, the current representation learning models ignore the abundant geographic information implicit in the entities and relations, and therefore there is still room for improvement. To overcome this problem, this paper proposes a novel link prediction framework for knowledge graph completion. By leveraging geographic information to generate geographic units and rules, we construct geographic constraints for optimizing and boosting the representation learning results. Extensive experiments show that the proposed framework improves the performance of the current representation learning models for link prediction task.