Enabling Spatio-Temporal Search in Open Data. Neumaier, S. & Polleres, A. Journal of Web Semantics (JWS), 55:21–36, Elsevier, March, 2019. ![Enabling Spatio-Temporal Search in Open Data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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, in the present paper 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 http://data.wu.ac.at/odgraphsearch/
@article{neum-poll-2019jws,
title = {Enabling Spatio-Temporal Search in Open Data},
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, in the present paper 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 http://data.wu.ac.at/odgraphsearch/},
author = {Sebastian Neumaier and Axel Polleres},
year = 2019,
journal = JWS,
publisher = {Elsevier},
url = {http://epub.wu.ac.at/6764/},
volume = 55,
doi = {10.1016/j.websem.2018.12.007},
pages ={21--36},
month = mar,
}
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