Exploration of TransE in a Distributed Environment

Lu, Meiyan; Liao, Lejian; Zhang, Feng; Song, Dandan

doi:10.1109/icdcs47774.2020.00190

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…To provide a streamlined entity representation, we apply the Imperial Guangdong Maritime Customs archive TransE. The TransE [9] model offers a scalable and versatile approach for embedding nodes and edges from diverse graphs into a unified vector space. This model effectively captures both local and global connectivity patterns among entities by interpreting the relationships within the graph as transformations embedded within the vector space.…”

Section: Statesmentioning

confidence: 99%

A reinforcement learning model for the knowledge graph of imperial Guangdong maritime customs archival translation

Chen

2024

Second International Conference on Informatics, Networking, and Computing (ICINC 2023)

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The Guangdong Provincial Archives stores a large volume of valuable historical original Customs archives which are enormous in number and volume, formal in format, various in types and genres, rich in content, providing precious value for such studies as those of Imperial Chinese and foreign exchanges. It has always been the focus of research in the academic world at home and abroad. This paper, characterized by the inputting and outputting relationship among the entities (proper names of people, places, titles, weights and measures) that are most closely related to the translation of Imperial Guangdong Maritime Customs archives, constructs the knowledge graph of Imperial Guangdong Maritime Customs archival translation. In this paper, representative subgraphs being calculated based on the given node sets, a novel archival translation knowledge graph method is proposed, and the spatial components are integrated into the reinforcement learning framework to help understand the knowledge graph of archival translation. Through establishing the Imperial Guangdong Maritime Customs archival translation theory model, the internal structure and external information of archives are analyzed to obtain a more comprehensive and holistic view of the archival translation tasks. At last, the model proposed in this paper is proved to be reasonable by evaluating the data sets of Imperial Guangdong Maritime Customs archives.

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Section: Statesmentioning

confidence: 99%

A reinforcement learning model for the knowledge graph of imperial Guangdong maritime customs archival translation

Chen

2024

Second International Conference on Informatics, Networking, and Computing (ICINC 2023)

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MRNDR: Multihead Attention-Based Recommendation Network for Drug Repurposing

Feng,

Ma,

et al. 2024

J. Chem. Inf. Model.

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As is well-known, the process of developing new drugs is extremely expensive, whereas drug repurposing represents a promising approach to augment the efficiency of new drug development. While this method can indeed spare us from expensive drug toxicity and safety experiments, it still demands a substantial amount of time to carry out precise efficacy experiments for specific diseases, thereby consuming a significant quantity of resources. Therefore, if we can prescreen potential other indications for selected drugs, it could result in substantial cost savings. In light of this, this paper introduces a drug repurposing recommendation model called MRNDR, which stands for Multi-head attention-based Recommendation Network for Drug Repurposing. This model serves as a prediction tool for drug-disease relationships, leveraging the multihead self-attention mechanism that demonstrates robust generalization capabilities. These capabilities stem not only from our extensive million-level training data set, BioRE (Biology Recommended Entity data), but also from the utilization of the WRDS (Weighted Representation Distance Score) algorithm proposed by us. The MRNDR model has achieved new state-of-the-art results on the GP-KG public data set, with an MRR (Mean Reciprocal Rank) score of 0.308 and a Hits@10 score of 0.628. This represents significant improvements of 4.7% (MRR) and 18.1% (Hits@10) over the current best-performing models. Additionally, to further validate the practical utility of the model, we examined results recommended by MRNDR that were not present in the training data set. Some of these recommendations have undergone clinical trials, as evidenced by their presence on ClinicalTrials.gov and the China Clinical Trials Center, indirectly confirming the applicability of MRNDR. The MRNDR model can predict the reusability of candidate drugs, reducing the need for manual expert assessments and enabling efficient drug repurposing.

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Exploration of TransE in a Distributed Environment

Cited by 2 publications

References 7 publications

A reinforcement learning model for the knowledge graph of imperial Guangdong maritime customs archival translation

A reinforcement learning model for the knowledge graph of imperial Guangdong maritime customs archival translation

MRNDR: Multihead Attention-Based Recommendation Network for Drug Repurposing

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