TransR*: Representation learning model by flexible translation and relation matrix projection

Abstract

The TransR model solves the problem that TransE and TransH models are not sufficient for modeling in public spaces, and is considered a highly potential knowledge representation model. However, TransR still adopts the translation principles based on the TransE model, and the constraints are too strict, which makes the model’s ability to distinguish between very similar entities low. Therefore, we propose a representation learning model TransR* based on flexible translation and relational matrix projection. Firstly, we separate entities and relationships in different vector spaces; secondly, we combine our flexible translation strategy to make translation strategies more flexible. During model training, the quality of generating negative triples is improved by replacing semantically similar entities, and the prior probability of the relationship is used to distinguish the relationship of similar coding. Finally, we conducted link prediction experiments on the public data sets FB15K and WN18, and conducted triple classification experiments on the WN11, FB13, and FB15K data sets to analyze and verify the effectiveness of the proposed model. The evaluation results show that our method has a better improvement effect than TransR on Mean Rank, Hits@10 and ACC indicators.

A Knowledge Graph Embedding Model Based on Cyclic Consistency—Cyclic_CKGE

“…Based on the TransE [23] model, a number of subsequent research results were extended, including the use of projection vectors or matrices to transform the head and tail entities into relational vector Spaces. During this period, many models began with Trans, such as TransH [24], TransR [31] and TransD [32]. TransH treats relationships as hyperplanes and projects head and tail entities into the hyperplane of a particular relationship.…”

“…However, the types or attributes of the head and tail entities of the relationship may be very different. The projection from the entity space to the relation space is an interaction process between the entity and the relation, so it is unreasonable for TransR [31] to only associate the projection matrix with the relation. Compared with TransE [23] and TransH [24], due to the introduction of spatial projection, the TransR [31] model has significantly more parameters and higher computational complexity.…”

“…The projection from the entity space to the relation space is an interaction process between the entity and the relation, so it is unreasonable for TransR [31] to only associate the projection matrix with the relation. Compared with TransE [23] and TransH [24], due to the introduction of spatial projection, the TransR [31] model has significantly more parameters and higher computational complexity. TransD [32] further simplifies TransR [31] by decomposing the projection matrix into the product of two vectors.…”

“…Compared with TransE [23] and TransH [24], due to the introduction of spatial projection, the TransR [31] model has significantly more parameters and higher computational complexity. TransD [32] further simplifies TransR [31] by decomposing the projection matrix into the product of two vectors.…”

Composite Harmonic Source Detection with Multi-Label Approach Using Advanced Fusion Method

“…In the Trans series that maps entity relationship triplets to low dimensional space vector representations, TransR can better handle one-to-many and many-to-many relationships and has stronger semantic expression ability [23]. This article is based on TransR to represent the frequency-domain features of eight different time series of harmonic sources as part of the input of the transformer model.…”