  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.
