Learning entity type structured embeddings with trustworthiness on noisy knowledge graphs

Yu, Zhiwu; Li, Zhiquan; Deng, Wei; Xie, Rong; Li, Qing

doi:10.1016/j.knosys.2020.106630

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William Adam Was Technical Writer Poet Philosopher1

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2022

2023

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Cited by 9 publications

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“…Li et al, 2020). This demanding structure demonstrates the brilliant integration and durability of description embodied knowledge representing learning model, that is crucial to the huge knowledge graph scale and its overall performance of the website (Zhao et al, 2021).…”

Section: William Adam Was Technical Writer Poet Philosophermentioning

confidence: 97%

Learning of Knowledge Graphs with Entity Descriptions

Conde¹

2022

IJMRA

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The representing learning makes specialty of knowledge graph and it indicates the difference between different entities. The knowledge graph representing with the low-dimensional space. In fact, most of the method usually concern of description of entity which is hard for existing strategies to take benefit of. Here, we recommend a new representing learning method with knowledge graphs that uses entity description. We evaluate our method on two tasks like knowledge graph and entity classification. Experimental effects on actual-world datasets show that our version plays higher than different baseline fashions, especially under the zero-short setting, which indicate that our technique for novel the entity description.

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Section: William Adam Was Technical Writer Poet Philosophermentioning

confidence: 97%