Enriching integrated statistical open city data by combining equational knowledge and missing value imputation

Bischof, Stefan and Harth, Andreas and Kämpgen, Benedikt and Polleres, Axel ORCID: https://orcid.org/0000-0001-5670-1146 and Schneider, Patrik (2017) Enriching integrated statistical open city data by combining equational knowledge and missing value imputation. Journal of Web Semantics, 48. pp. 22-47. ISSN 1570-8268


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Several institutions collect statistical data about cities, regions, and countries for various purposes. Yet, while access to high quality and recent such data is both crucial for decision makers and a means for achieving transparency to the public, all too often such collections of data remain isolated and not re-useable, let alone comparable or properly integrated. In this paper we present the Open City Data Pipeline, a focused attempt to collect, integrate, and enrich statistical data collected at city level worldwide, and re-publish the resulting dataset in a re-useable manner as Linked Data. The main features of the Open City Data Pipeline are: (i) we integrate and cleanse data from several sources in a modular and extensible, always up-to-date fashion; (ii) we use both Machine Learning techniques and reasoning over equational background knowledge to enrich the data by imputing missing values, (iii) we assess the estimated accuracy of such imputations per indicator. Additionally, (iv) we make the integrated and enriched data, including links to external data sources, such as DBpedia, available both in a web browser interface and as machine-readable Linked Data, using standard vocabularies such as QB and PROV. Apart from providing a contribution to the growing collection of data available as Linked Data, our enrichment process for missing values also contributes a novel methodology for combining rule-based inference about equational knowledge with inferences obtained from statistical Machine Learning approaches. While most existing works about inference in Linked Data have focused on ontological reasoning in RDFS and OWL, we believe that these complementary methods and particularly their combination could be fruitfully applied also in many other domains for integrating Statistical Linked Data, independent from our concrete use case of integrating city data.

Item Type: Article
Additional Information: Part of the work was carried out with the support of the German Federal Ministry of Education and Research (BMBF) within the project "WIRE" (Grant 01IS16039B). To see the final version of this paper please visit the publisher's website. Access to the published version may require a subscription. The original publication is available at http://www.elsevier.com.
Keywords: Open data, Linked Data, Data cleaning, Data integration
Divisions: Departments > Informationsverarbeitung u Prozessmanag. > Informationswirtschaft > Polleres
Version of the Document: Submitted
Variance from Published Version: None
Depositing User: Mohammad Al Hessan
Date Deposited: 19 Mar 2018 13:33
Last Modified: 24 Oct 2019 13:30
Related URLs:
URI: https://epub.wu.ac.at/id/eprint/6141


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