Intuitively, most datasets found on governmental Open Data portals are organized by spatio-temporal criteria, that is, single datasets provide data for a certain region, valid for a certain time period. Likewise, for many use cases (such as, for instance, data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Rich spatio-temporal annotations are therefore a crucial need to enable semantic search for (and across) Open Data portals along those dimensions, yet – to the best of our knowledge – no working solution exists. To this end, we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals with entities from this knowledge graph, and (iii) enable structured, spatio-temporal search and querying over Open Data catalogs, both via a search interface as well as via a SPARQL endpoint, available at data.wu.ac.at/odgraphsearch/.

直观上，在政府开放数据门户网站上找到的大多数数据集都是按时空标准组织的，也就是说，单个数据集提供了在特定时间段内有效的特定区域的数据。同样，对于许多用例（例如，数据新闻和事实检查），主要需求是将相关数据集的范围缩小到特定时期或区域。因此，丰富的时空注释是使语义搜索沿这些维度（以及跨这些维度）进行开放数据门户的关键需求，但是据我们所知，尚无可行的解决方案。为此，我们（i）提出了一种可扩展的方法来构造时空知识图，该图以层次结构构造地理和时间实体，