EARP: Integration with Entity Attribute and Relation Path for Event Knowledge Graph Representation Learning

Event knowledge graph (EKG) as a special case of knowledge graph (KG) can realize the goal of event prediction, and has been proved useful in medical diagnosis and intelligent recommendation. To successfully build an EKG, knowledge representation learning is often required to compute the semantic links of entities and relationships in a low-dimensional space and solve the data sparsity issue in knowledge acquisition, fusion and reasoning. This paper proposes a new EKG representation learning model featuring the integration of event entity attributes and relation paths. By utilizing the knowledge of entity attribute, which contains entity type and entity description, and the knowledge about relation paths, the entity initial vector is obtained by multiplying entity semantic vector, entity description representation vector and entity type representation vector, and the representation of relation path is obtained according to the relation between event pairs, a translation-based model framework is used to integrate and train all vectors to obtain the entity learning vector and the relation learning vector. our method can generate more expressive learning representations, and consequently, enhance the inference performance of EKG. Experiments on publicly available real-world EKG datasets show that our method achieves better performance than the state-of-the-art models on two typical tasks.