Analysis of Translational and Tensor Factorization Knowledge Graph Embedding models
Fawaz Wangde, Anish Khobragade, Omkar Shinde · 2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA · 2022
Knowledge graph embedding is used to find a real value vector representation of entities and relationship that is utilized to do the link prediction task. Knowledge Graph comprises the real world entities but many relationship between the set of two entities are missing that effect the utilization of knowledge graph for the application such as information retrieval and question-and-answering. The translation distance embedding model and tensor factorization based model focuses on predicting the missing relationship between the set of two entities by utilizing learned embedding. This paper illustrates, analyzes and evaluates the models over the benchmark dataset FB15k.