The purpose of knowledge graph entity disambiguation is to match the ambiguous entities to the corresponding entities in the knowledge graph. Current entity ambiguity elimination methods usually use the context information of the entity and its attributes to obtain the mention embedding vector, compare it with the candidate entity embedding vector for similarity, and perform entity matching through the similarity. The disadvantage of this type of method is that it ignores the structural characteristics of the knowledge graph where the entity is located, that is, the connection between the entity and the entity, and therefore cannot obtain the global semantic features of the entity. To improve the Precision and Recall of entity disambiguation problems, we propose the EDEGE (Entity Disambiguation based on Entity and Graph Embedding) method, which utilizes the semantic embedding vector of entity relationship and the embedding vector of subgraph structure feature. EDEGE first trains the semantic vector of the entity relationship, then trains the graph structure vector of the subgraph where the entity is located, and balances the weights of these two vectors through the entity similarity function. Finally, the balanced vector is input into the graph neural network, and the matching between the entities is output to achieve entity disambiguation. Extensive experimental results proved the effectiveness of the proposed method. Among them, on the ACE2004 data set, the Precision, Recall, and F1 values of EDEGE are 9.2%, 7%, and 11.2% higher than baseline methods.

Electrochemical ammonia synthesis via nitrogen reduction reaction (NRR) has received abundant attention, but high nitrogen activation barrier and competitive hydrogen evolution reaction (HER) heavily limit its performance. Herein, the mesoporous...