Neumaier, Sebastian, Polleres, Axel. 2019. Enabling Spatio-Temporal Search in Open Data. Journal of Web Semantics. 55 21-36.

RIS

@ARTICLE{Neumaier2019,
title = {Enabling Spatio-Temporal Search in Open Data},
author = {Sebastian Neumaier and Axel Polleres},
year = {2019},
doi = {https://doi.org/10.1016/j.websem.2018.12.007},
url = {http://www.sciencedirect.com/science/article/pii/S1570826818300696},
volume = {55},
language = {EN},
pages = {21-36},
journal = {Journal of Web Semantics},
abstract = {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/.},
}