GEUKE: A geographic entities uniformly explicit knowledge embedding model

IF 2.1 3区 地球科学 Q2 GEOGRAPHY
Yongquan Yang, Dehui Kong, Min Cao, Min Chen
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引用次数: 0

Abstract

Knowledge embedding for geographic knowledge graphs can effectively improve computational efficiency and provide support for knowledge reasoning, knowledge answering and other applications of knowledge graphs. To maintain a more comprehensive understanding of spatial features through knowledge embedding, it is crucial to integrate the representation and computation of various entity types, encompassing points, lines, and polygons. This article proposes a geographic entities uniformly explicit knowledge embedding model (GEUKE). In GEUKE, spatial data of point, line, and polygon‐type geographic entities are expressed in the form of subgraphs, and space embedding is generated using a SubGNN‐based uniform spatial feature encoder. GEUKE improves the energy function in TransE to train spatial feature‐based embedding and structural‐based embedding of geographic entities into a unified vector space. Experimental results show that GEUKE has higher performance than TransE, TransH, TransD, and TransE‐GDR on link prediction and triple classification task. Within the spatial feature embedding process, GEUKE effectively preserves the inherent features of entities, encompassing location, neighborhood, and structural attributes, while simultaneously ensuring a coherent spatial data representation across all three entity types: points, lines, and polygons. By maintaining the spatial features of geographic entities and their interrelations, this capability unleashes the full potential of applications such as knowledge reasoning and geospatial question answering in a manner that is conducive to diverse geospatial scenarios.
GEUKE:地理实体统一显性知识嵌入模型
地理知识图谱的知识嵌入可以有效提高计算效率,并为知识推理、知识解答和知识图谱的其他应用提供支持。要通过知识嵌入保持对空间特征更全面的理解,关键是要整合包括点、线和多边形在内的各种实体类型的表示和计算。本文提出了一种地理实体统一显式知识嵌入模型(GEUKE)。在 GEUKE 中,点、线和多边形地理实体的空间数据以子图的形式表示,并使用基于 SubGNN 的统一空间特征编码器生成空间嵌入。GEUKE 改进了 TransE 中的能量函数,将地理实体的基于空间特征的嵌入和基于结构的嵌入训练到统一的向量空间中。实验结果表明,在链接预测和三重分类任务上,GEUKE 的性能高于 TransE、TransH、TransD 和 TransE-GDR。在空间特征嵌入过程中,GEUKE 有效地保留了实体的固有特征,包括位置、邻域和结构属性,同时确保了所有三种实体类型(点、线和多边形)的空间数据表示的一致性。通过保持地理实体的空间特征及其相互关系,这一功能可充分释放知识推理和地理空间问题解答等应用的潜力,从而有利于各种地理空间场景的应用。
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来源期刊
Transactions in GIS
Transactions in GIS GEOGRAPHY-
CiteScore
4.60
自引率
8.30%
发文量
116
期刊介绍: Transactions in GIS is an international journal which provides a forum for high quality, original research articles, review articles, short notes and book reviews that focus on: - practical and theoretical issues influencing the development of GIS - the collection, analysis, modelling, interpretation and display of spatial data within GIS - the connections between GIS and related technologies - new GIS applications which help to solve problems affecting the natural or built environments, or business
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