A Relational Memory-based Embedding Model for Triple Classification and Search Personalization
This addresses the problem of improving accuracy in knowledge graph tasks for applications like search personalization, though it appears incremental as it builds on existing embedding methods with a novel memory mechanism.
The authors tackled the limitation of knowledge graph embedding methods in memorizing valid triples for triple classification and search personalization by introducing R-MeN, a relational memory-based model that achieved state-of-the-art results on datasets like SEARCH17, WN11, and FB13.
Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems. To this end, we introduce a novel embedding model, named R-MeN, that explores a relational memory network to encode potential dependencies in relationship triples. R-MeN considers each triple as a sequence of 3 input vectors that recurrently interact with a memory using a transformer self-attention mechanism. Thus R-MeN encodes new information from interactions between the memory and each input vector to return a corresponding vector. Consequently, R-MeN feeds these 3 returned vectors to a convolutional neural network-based decoder to produce a scalar score for the triple. Experimental results show that our proposed R-MeN obtains state-of-the-art results on SEARCH17 for the search personalization task, and on WN11 and FB13 for the triple classification task.