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>描述</strong> GeoVectors语料库是一款大规模综合性关联开源语料库，基于OpenStreetMap（https://www.openstreetmap.org/）构建，涵盖超9.8亿个实体的潜在表征。该语料库可捕捉OpenStreetMap实体的语义与地理相似性，可供机器学习应用直接调用。其中带"-tags"的数据集提供刻画实体语义相似性的嵌入向量，带"-location"的数据集则提供表征地理相似性的嵌入向量。 <strong>数据集内容</strong> 本数据集源自2020年11月10日的OpenStreetMap快照（© OpenStreetMap贡献者）。我们按区域子集发布GeoVectors嵌入，本次子集为"亚洲"区域的标签嵌入，覆盖以下国家/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、印度、印度尼西亚、伊朗、伊拉克、以色列与巴勒斯坦、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、马尔代夫、蒙古、缅甸、尼泊尔、朝鲜、巴基斯坦、菲律宾、韩国、斯里兰卡、叙利亚、中国台湾地区、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> 嵌入向量以制表符分隔值（TSV，tab-separated values）格式存储。每一行对应单个OpenStreetMap实体的嵌入向量：第一列为OpenStreetMap实体类型，第二列为对应实体的OpenStreetMap ID，实体类型可为节点（node，缩写n）、路径（way，缩写w）或关系（relation，缩写r），剩余列则为嵌入空间的维度向量（详见header.tsv文件）。 <strong>更多信息</strong> 如需获取进一步资料，请访问http://geovectors.l3s.uni-hannover.de <strong>资助情况</strong> 本研究部分受德国研究基金会（DFG，"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（EU H2020框架计划，"smashHit"项目，资助编号871477）资助。