CoolGust: knowledge representation learning with commonsense knowledge guidelines and constraints

Representation learning serves as a crucial link between knowledge graphs and neural models. Knowledge graphs are typical symbolic models and require representation learning to obtain vector representations of entities and relationships. Existing efforts focus more on the structural characteristics of knowledge itself, such as the connections between entities and relationships under the same type of knowledge, neglecting the potential value of commonsense knowledge in guiding and constraining. In fact, commonsense knowledge implies the belonging tendency of entities in triplets. In this paper, we propose a novel knowledge representation learning model, CoolGust, which is guided and constrained by commonsense knowledge and can effectively utilize commonsense knowledge to seamlessly guide the belonging relationships of entities and enhance the model performance. Commonsense knowledge is essential for guiding humans to make wise decisions in unknown scenarios. We find that by utilizing commonsense knowledge as guides and constraints for entities, hidden knowledge entanglement structures can be formed in complex network applications, thereby constructing decisions consistent with situational logic. To verify the effectiveness of our model, we conducted experimental verification of two tasks, link prediction, and triple classification, on three public datasets. The experimental results demonstrate the effectiveness and advancedness of our proposed method.