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

## TL;DR

This paper introduces R-MeN, a relational memory network that enhances knowledge graph embedding by encoding dependencies in triples, leading to improved triple classification and search personalization performance.

## Contribution

The novel R-MeN model uses a relational memory network with transformer self-attention to better encode dependencies in triples for knowledge graph tasks.

## Key findings

- Achieves state-of-the-art results on SEARCH17 for search personalization.
- Outperforms existing models on WN11 and FB13 for triple classification.
- Demonstrates improved encoding of relational dependencies.

## Abstract

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.