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Applications of Knowledge Representation Learning

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MDATA: A New Knowledge Representation Model

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12647))

Abstract

Knowledge representation learning (KRL) is one of the most important research topics in artificial intelligence, especial in natural language processing (NLP). After extracting entities and relations, some kinds of knowledge, KRL can efficiently calculate the semantics of the entities and the relations in a low-dimensional space, which effectively solve the problem of data sparsity, and can significantly improve the performance of knowledge acquisition, fusion and reasoning. Starting from the three common perspectives of KRL, scoring function, model coding type and additional information, this chapter introduces the overall framework of KRL and the specific model design. In addition, we introduce the corresponding experimental evaluation tasks, including the evaluation metrics and benchmark datasets of each model. Afterwards, we summarize how to apply KRL in various downstream NLP tasks.

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  1. National University of Defense Technology, Changsha, 410073, China

    Chenchen Li, Aiping Li, Ye Wang & Hongkui Tu

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  1. Chenchen Li
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  2. Aiping Li
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  3. Ye Wang
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  4. Hongkui Tu
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Correspondence to Hongkui Tu .

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  1. Harbin Institute of Technology, Shenzhen, Shenzhen, China

    Yan Jia

  2. Guangzhou University, Guangzhou, China

    Zhaoquan Gu

  3. National University of Defense Technology, Changsha, China

    Aiping Li

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Li, C., Li, A., Wang, Y., Tu, H. (2021). Applications of Knowledge Representation Learning. In: Jia, Y., Gu, Z., Li, A. (eds) MDATA: A New Knowledge Representation Model. Lecture Notes in Computer Science(), vol 12647. Springer, Cham. https://doi.org/10.1007/978-3-030-71590-8_10

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  • DOI: https://doi.org/10.1007/978-3-030-71590-8_10

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  • Print ISBN: 978-3-030-71589-2

  • Online ISBN: 978-3-030-71590-8

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