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Knowledge graph embedding methods often suffer from a limitation ofmemorizing valid triples to predict new ones for triple classification andsearch personalization problems. To this end, we introduce a novel embeddingmodel, named R-MeN, that explores a relational memory network to encodepotential dependencies in relationship triples. R-MeN considers each triple asa sequence of 3 input vectors that recurrently interact with a memory using atransformer self-attention mechanism. Thus R-MeN encodes new information frominteractions between the memory and each input vector to return a correspondingvector. Consequently, R-MeN feeds these 3 returned vectors to a convolutionalneural network-based decoder to produce a scalar score for the triple.Experimental results show that our proposed R-MeN obtains state-of-the-artresults on SEARCH17 for the search personalization task, and on WN11 and FB13for the triple classification task.

A Relational Memory-based Embedding Model for Triple Classification and Search Personalization