GeoVectors-Asia-tags (v1.0)

<strong>Description</strong> The GeoVectors corpus is a comprehensive large-scale linked open corpus of OpenStreetMap (https://www.openstreetmap.org/) entity embeddings that provides latent representations of over 980 million entities. The GeoVectors capture the semantic and geographic similarities of OpenStreetMap entities and make them directly accessible to machine learning applications. The "-tags" datasets provide embeddings that capture the semantic similarities of OpenStreetMap entities. The "-location" datasets provide the geographic similarities. <strong>Contents</strong> This dataset was derived from an OpenStreetMap snapshot that was taken on November 10, 2020 (© OpenStreetMap contributors). We provide the GeoVectors in region-specific subsets. This subset contains tag-embeddings for the region "Asia" including the following countries: Afghanistan Armenia Azerbaijan Bangladesh Bhutan Cambodia China Gcc-States India Indonesia Iran Iraq Israel-and-Palestine Japan Jordan Kazakhstan Kyrgyzstan Laos Lebanon Malaysia-Singapore-Brunei Maldives Mongolia Myanmar Nepal North-Korea Pakistan Philippines South-Korea Sri-Lanka Syria Taiwan Tajikistan Thailand Turkmenistan Uzbekistan Vietnam Yemen <strong>File format</strong> The embeddings are provided in the tab-separated values (tsv) format. Each row contains the embedding of a single OpenStreetMap entity. The first column contains the OpenStreetMap type and the second column contains the OpenStreetMap id of the respective entity. The type can either be node (n), way (w), or relation (r). The remaining columns represent the dimensions of the embedding space. (See also header.tsv) <strong>Further information:</strong> For further information, please visit http://geovectors.l3s.uni-hannover.de <strong>Funding</strong>: This work was partially funded by DFG, German Research Foundation (“WorldKG", DE 2299/2-1), the Federal Ministry of Education and Research (BMBF), Germany (“Simple-ML", 01IS18054), the Federal Ministry for Economic Affairs and Energy (BMWi), Germany (“d-E-mand", 01ME19009B), and the European Commission (EU H2020, “smashHit", grant-ID 871477).<strong>数据集描述</strong> GeoVectors语料库是一款大规模关联开放语料库，基于开放街道地图（OpenStreetMap，https://www.openstreetmap.org/）构建，包含超过9.8亿个实体的潜在表征。该语料库可捕捉开放街道地图实体的语义与地理相似性，可直接供机器学习应用调用。其中，"-标签（-tags）"数据集用于捕获实体的语义相似性嵌入，"-位置（-location）"数据集则用于捕获实体的地理相似性嵌入。<strong>数据集内容</strong> 本数据集源自2020年11月10日的开放街道地图快照（© 开放街道地图贡献者）。我们按区域子集形式发布GeoVectors，本次子集为"亚洲（Asia）"区域的标签嵌入数据集，涵盖以下国家及地区：阿富汗、亚美尼亚、阿塞拜疆、孟加拉国、不丹、柬埔寨、中国、海湾合作委员会成员国（Gcc-States）、印度、印度尼西亚、伊朗、伊拉克、以色列与巴勒斯坦（Israel-and-Palestine）、日本、约旦、哈萨克斯坦、吉尔吉斯斯坦、老挝、黎巴嫩、马来西亚-新加坡-文莱（Malaysia-Singapore-Brunei）、马尔代夫、蒙古、缅甸、尼泊尔、朝鲜民主主义人民共和国（North-Korea）、巴基斯坦、菲律宾、大韩民国（South-Korea）、斯里兰卡、叙利亚、中国台湾地区（Taiwan）、塔吉克斯坦、泰国、土库曼斯坦、乌兹别克斯坦、越南、也门。<strong>文件格式</strong> 嵌入向量采用制表符分隔值（TSV, tab-separated values）格式存储。每一行对应一个开放街道地图实体的嵌入向量：第一列为实体类型，第二列为对应实体的开放街道地图ID，实体类型可分为节点（node，缩写n）、路径（way，缩写w）与关系（relation，缩写r）三类，剩余列均为嵌入空间的维度参数（详见header.tsv文件）。<strong>更多信息</strong> 如需获取更多相关信息，请访问http://geovectors.l3s.uni-hannover.de。<strong>资助说明</strong> 本研究部分受德国研究基金会（DFG, German Research Foundation）"WorldKG"项目（编号DE 2299/2-1）、德国联邦教育与研究部（BMBF）"Simple-ML"项目（编号01IS18054）、德国联邦经济事务与能源部（BMWi）"d-E-mand"项目（编号01ME19009B）以及欧盟委员会"地平线2020（EU H2020）"框架下的"smashHit"项目（资助编号871477）资助。