A Knowledge Representation Method for Multiple Pattern Embeddings Based on Entity-Relation Mapping Matrix

The representation learning in knowledge graph aims to embed the semantic information of the research object into dense low dimensional distributed real valued vectors. The classical knowledge representation model, TransR, uses the relation specific matrix to represent the entities and relations in different semantic spaces, so as to solve the separation problem of semantic space. However, the TransR model only considers the different types of relations, ignores the entity types, and has defects in the processing of inverse relations. To solve the model problems, a novel knowledge representation method for multiple pattern embeddings based on entity-relation mapping matrix (M-TransER) is presented. The M-TransER model introduces the entity-relation mapping matrix, which makes the model consider the entity type while considering the relation type. On the basis that entities and relations are modeled as positive translation geometric distance, the inverse translation geometric distance model and the symmetrical relation model are fused. Eliminate the defects of the model in dealing with inverse relations, and improve the efficiency of symmetric relation processing. Experimental results on open datasets show that the M-TransER model has made great progress and demonstrated its superiority over the existing models and methods in both link prediction and triple classification tasks.