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首页> 外文期刊>Transactions in GIS: TG >A spatially explicit reinforcement learning model for geographic knowledge graph summarization
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A spatially explicit reinforcement learning model for geographic knowledge graph summarization

机译：地理知识图概述的空间显式增强学习模型

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摘要

Web-scale knowledge graphs such as the global Linked Data cloud consist of billions of individual statements about millions of entities. In recent years, this has fueled the interest in knowledge graph summarization techniques that compute representative subgraphs for a given collection of nodes. In addition, many of the most densely connected entities in knowledge graphs are places and regions, often characterized by thousands of incoming and outgoing relationships to other places, actors, events, and objects. In this article, we propose a novel summarization method that incorporates spatially explicit components into a reinforcement learning framework in order to help summarize geographic knowledge graphs, a topic that has not been considered in previous work. Our model considers the intrinsic graph structure as well as the extrinsic information to gain a more comprehensive and holistic view of the summarization task. By collecting a standard data set and evaluating our proposed models, we demonstrate that the spatially explicit model yields better results than non-spatial models, thereby demonstrating that spatial is indeed special as far as summarization is concerned.
机译：Web级知识图（如全局链接数据云）包括数十亿个单个陈述约数百万个实体。近年来，这引起了对知识图表摘要技术的兴趣来计算给定的节点集合的代表子图。此外，知识图中的许多最密集地连接的实体是地区和地区，通常以其他地方，演员，事件和对象的成千上万的传入和传出关系为特征。在本文中，我们提出了一种新的摘要方法，该方法将空间显式组件纳入加强学习框架，以帮助总结地理知识图，这是在以前的工作中未被考虑的主题。我们的模型考虑了内在图形结构以及外在信息，以获得摘要任务的更全面和全面的视图。通过收集标准数据集和评估我们所提出的模型，我们证明了空间显式模型比非空间模型产生更好的结果，从而表明空间确实特别特别，就摘要而言。

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  • 来源
    《Transactions in GIS: TG》 |2019年第3期|共21页
  • 作者

    Yan Bo; Janowicz Krzysztof; Mai Gengchen; Zhu Rui;

  • 作者单位

    Univ Calif Santa Barbara Dept Geog STKO Lab Santa Barbara CA 93106 USA;

    Univ Calif Santa Barbara Dept Geog STKO Lab Santa Barbara CA 93106 USA;

    Univ Calif Santa Barbara Dept Geog STKO Lab Santa Barbara CA 93106 USA;

    Univ Calif Santa Barbara Dept Geog STKO Lab Santa Barbara CA 93106 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 测绘数据库与信息系统;
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