DTransE: Distributed Translating Embedding for Knowledge Graph

Song, Dandan; Zhang, Feng; Lu, Meiyan; Yang, Sicheng; Huang, Heyan

doi:10.1109/tpds.2021.3066442

Cited by 15 publications

(4 citation statements)

References 27 publications

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“…Indeed, in exploring the target academic contexts in which undergraduates should engage, academic English researchers have focused on both the outcomes and practices of academic research as well as the outcomes and practices of teaching and learning in which students can acquire academic knowledge [8]. A useful starting point for examining the future of higher education is a definition of the discipline that encompasses teaching and learning [9]. To create opportunities for undergraduate students to successfully engage in scholarship, the role of academic English teachers in higher education is to make unfamiliar teaching and learning practices familiar, and to do this, academic English teachers need to draw on others or their research on these teaching practices [10].…”

Section: Related Workmentioning

confidence: 99%

A Model for Teaching English Translation in Universities Based on the Random Matrix Optimization Bell Translation Model

Xie

Li²

2022

Mathematical Problems in Engineering

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This paper adopts the random matrix optimization fusion of Bell’s translation model to conduct in-depth research and analysis on the teaching model of English translation in colleges and universities. Bell’s translation model theory explicates the mental model of the translation process to a certain extent based on psycholinguistic research results. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct the subspace-weighting matrix. The statistical properties of the sample covariance matrix eigenvectors were analyzed using the first-order perturbation approximation, and then asymptotic results from random matrix theory on the projection of the sample covariance matrix signal subspaces to the real signal parametrization were used to obtain the weighting matrix based on the random matrix eigenvectors. The analysis shows that the teacher can help students understand the teaching content and master the knowledge points by using guided meta-discoursal to indicate the logical relationships inherent in linguistic expressions and make the discourse transitions natural, coherent, and organized. The use of interactive meta-discoursal attracts students’ attention and treats them as participants in the discourse, making them feel a sense of belonging and satisfying their need for psychological belonging, which is beneficial to the students’ main position in the classroom. In teaching methods, there is a principle of comprehensible input, which is to provide students with a lot of interesting and understandable listening and reading input. In the translation classroom, the translation teachers code-switched infrequently and mainly used three types of code-switching: inter-sentence code-switching, intra-sentence code-switching, and additional code-switching to carry out translation teaching. Among them, intra-sentence code-switching accounts for 57.46%, inter-sentence code-switching accounts for 41.53%, and the least frequent is additional code-switching, accounting for 1.01%.

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Section: Related Workmentioning

confidence: 99%

A Model for Teaching English Translation in Universities Based on the Random Matrix Optimization Bell Translation Model

Xie

Li²

2022

Mathematical Problems in Engineering

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“…There have been many research results in the embedding field of word embedding [19], graph embedding [20,21] and item embedding. However, most embeddings are based on the contextual relationship between entities, and do not integrate multiple feature attributes within entities.…”

Section: Introductionmentioning

confidence: 99%

Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information

Huang

Chen

Zhang

2023

Entropy

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Hash is one of the most widely used methods for computing efficiency and storage efficiency. With the development of deep learning, the deep hash method shows more advantages than traditional methods. This paper proposes a method to convert entities with attribute information into embedded vectors (FPHD). The design uses the hash method to quickly extract entity features, and uses a deep neural network to learn the implicit association between entity features. This design solves two main problems in large-scale dynamic data addition: (1) The linear growth of the size of the embedded vector table and the size of the vocabulary table leads to huge memory consumption. (2) It is difficult to deal with the problem of adding new entities to the retraining model. Finally, taking the movie data as an example, this paper introduces the encoding method and the specific algorithm flow in detail, and realizes the effect of rapid reuse of dynamic addition data model. Compared with three existing embedding algorithms that can fuse entity attribute information, the deep hash embedding algorithm proposed in this paper has significantly improved in time complexity and space complexity.

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“…The relational vector in the knowledge graph was based on TransE, which effectively minimized the computing complexity by representing it as a transition of modular space. A distributed translation-based knowledge graph embedding model (DTransE) was suggested in study [39]. The Gather-Apply-Scatter model, which is based on the TransE model, is used in this method to significantly increase the model's calculation speed.…”

Section: Introductionmentioning

confidence: 99%

TransRFT: A Knowledge Representation Learning Model Based on a Relational Neighborhood and Flexible Translation

Wan,

Niu,

Chen

et al. 2023

Applied Sciences

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The use of knowledge graphs has grown significantly in recent years. However, entities and relationships must be transformed into forms that can be processed by computers before the construction and application of a knowledge graph. Due to its simplicity, effectiveness, and great interpretability, the translation model lead by TransE has garnered the most attention among the many knowledge representation models that have been presented. However, the performance of this model is poor when dealing with complex relations such as one-to-many, many-to-one, and reflexive relations. Therefore, a knowledge representation learning model based on a relational neighborhood and flexible translation (TransRFT) is proposed in this paper. Firstly, the triples are mapped to the relational hyperplane using the idea of TransH. Then, flexible translation is applied to relax the strict restriction h + r = t in TransE. Finally, the relational neighborhood information is added to further improve the performance of the model. The experimental results show that the model has good performance in triplet classification and link prediction.

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DTransE: Distributed Translating Embedding for Knowledge Graph

Cited by 15 publications

References 27 publications

A Model for Teaching English Translation in Universities Based on the Random Matrix Optimization Bell Translation Model

A Model for Teaching English Translation in Universities Based on the Random Matrix Optimization Bell Translation Model

Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information

TransRFT: A Knowledge Representation Learning Model Based on a Relational Neighborhood and Flexible Translation

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