Geographic data plays an essential role in various Web, Semantic Web and machine learning applications. OpenStreetMap and knowledge graphs are critical complementary sources of geographic data on the Web. However, data veracity, the lack of integration of geographic and semantic characteristics, and incomplete representations substantially limit the data utility. Verification, enrichment and semantic representation are essential for making geographic data accessible for the Semantic Web and machine learning. This article describes recent approaches we developed to tackle these challenges.
%0 Journal Article
%1 10.1145/3522598.3522602
%A Demidova, Elena
%A Dsouza, Alishiba
%A Gottschalk, Simon
%A Tempelmeier, Nicolas
%A Yu, Ran
%C New York, NY, USA
%D 2022
%I Association for Computing Machinery
%J SIGWEB Newsletter
%K myown simpleml
%N Winter
%R 10.1145/3522598.3522602
%T Creating Knowledge Graphs for Geographic Data on the Web
%U https://doi.org/10.1145/3522598.3522602
%X Geographic data plays an essential role in various Web, Semantic Web and machine learning applications. OpenStreetMap and knowledge graphs are critical complementary sources of geographic data on the Web. However, data veracity, the lack of integration of geographic and semantic characteristics, and incomplete representations substantially limit the data utility. Verification, enrichment and semantic representation are essential for making geographic data accessible for the Semantic Web and machine learning. This article describes recent approaches we developed to tackle these challenges.
@article{10.1145/3522598.3522602,
abstract = {Geographic data plays an essential role in various Web, Semantic Web and machine learning applications. OpenStreetMap and knowledge graphs are critical complementary sources of geographic data on the Web. However, data veracity, the lack of integration of geographic and semantic characteristics, and incomplete representations substantially limit the data utility. Verification, enrichment and semantic representation are essential for making geographic data accessible for the Semantic Web and machine learning. This article describes recent approaches we developed to tackle these challenges.},
added-at = {2022-04-01T13:18:31.000+0200},
address = {New York, NY, USA},
articleno = {4},
author = {Demidova, Elena and Dsouza, Alishiba and Gottschalk, Simon and Tempelmeier, Nicolas and Yu, Ran},
biburl = {https://www.bibsonomy.org/bibtex/23c69ecabda7ed36262912fbce294eb00/sgottschalk},
doi = {10.1145/3522598.3522602},
interhash = {f9efb3ec0ec8ee5016672c1065406114},
intrahash = {3c69ecabda7ed36262912fbce294eb00},
issn = {1931-1745},
issue_date = {Winter 2022},
journal = {SIGWEB Newsletter},
keywords = {myown simpleml},
month = mar,
number = {Winter},
numpages = {8},
publisher = {Association for Computing Machinery},
timestamp = {2022-04-01T13:18:31.000+0200},
title = {Creating Knowledge Graphs for Geographic Data on the Web},
url = {https://doi.org/10.1145/3522598.3522602},
year = 2022
}