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A Joint Model for Representation Learning of Tibetan Knowledge Graph Based on Encyclopedia

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Published:30 March 2021Publication History
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Abstract

Learning the representation of a knowledge graph is critical to the field of natural language processing. There is a lot of research for English knowledge graph representation. However, for the low-resource languages, such as Tibetan, how to represent sparse knowledge graphs is a key problem. In this article, aiming at scarcity of Tibetan knowledge graphs, we extend the Tibetan knowledge graph by using the triples of the high-resource language knowledge graphs and Point of Information map information. To improve the representation learning of the Tibetan knowledge graph, we propose a joint model to merge structure and entity description information based on the Translating Embeddings and Convolution Neural Networks models. In addition, to solve the segmentation errors, we use character and word embedding to learn more complex information in Tibetan. Finally, the experimental results show that our model can make a better representation of the Tibetan knowledge graph than the baseline.

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      • Published in

        cover image ACM Transactions on Asian and Low-Resource Language Information Processing
        ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 20, Issue 2
        March 2021
        313 pages
        ISSN:2375-4699
        EISSN:2375-4702
        DOI:10.1145/3454116
        Issue’s Table of Contents

        Copyright © 2021 Association for Computing Machinery.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 30 March 2021
        • Revised: 1 January 2021
        • Accepted: 1 January 2021
        • Received: 1 January 2020
        Published in tallip Volume 20, Issue 2

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