=Paper=
{{Paper
|id=Vol-1963/paper562
|storemode=property
|title=Linked Data for the Norwegian State of Estate Reporting Service
|pdfUrl=https://ceur-ws.org/Vol-1963/paper562.pdf
|volume=Vol-1963
|authors=Ling Shi,Bjorg Elsa Pettersen,Dina Sukhobok,Nikolay Nikolov,Dumitru Roman
|dblpUrl=https://dblp.org/rec/conf/semweb/ShiPSNR17
}}
==Linked Data for the Norwegian State of Estate Reporting Service==
https://ceur-ws.org/Vol-1963/paper562.pdf
Linked Data for the Norwegian State of Estate
Reporting Service
Ling Shi1 , Bjorg Elsa Pettersen1 , Dina Sukhobok2 , Nikolay Nikolov2 and
Dumitru Roman2
1
Statsbygg, Pb. 8106 Dep, 0032 Oslo, Norway
{Ling.Shi,BjorgElsa.Pettersen}@statsbygg.no
2
SINTEF, Forskningsveien 1a, 0373 Oslo, Norway
{dina.sukhobok,nikolay.nikolov,dumitru.roman}@sintef.no
Abstract. The Norwegian State of Estate (SoE) report includes infor-
mation about all Norwegian state-owned properties and buildings in the
public sector and aims to assist government decision makers to allocate
resources more effectively. A Linked Data based approach is presented
here to increase the transparency in the government administration, im-
prove the report generating process and also the report quality. Cross-
domain government data originated from the business entity register, the
cadastral system, the building accessibility register and the old SoE re-
port are acquired, prepared, cleaned, transformed to Linked Data format
and published. The source datasets are then integrated, augmented and
interlinked before the results are published as a SPARQL endpoint, used
for data visualization and report generation.
Keywords: Linked Data, data integration, government data
1 Introduction
The SoE report issued by Statsbygg3 on behalf of the Ministry of Local Gov-
ernment and Modernisation (KMD) is a governmental white paper, providing a
complete list of state-owned properties and buildings in the Norwegian public
sector. The report has been produced every four years as a result of manual
collection of information from multiple sources. The data collection and qual-
ity control process has historically been resource demanding and error prone
and the result was static and did not reflect the changes after the report was
published. This paper describes the publishing and integration of several cross-
domain government datasets related to state-owned real estates as Linked Open
Data. Sharing the state-owned properties related data in a Linked Data format
enables data reuse, opens up possibilities for using the data in innovative ways,
and helps to increase transparency in the government administration[4].
In addition, we demonstrate a Web-based application for registration and re-
porting state-owned properties in Norway. This represents a major improvement
compared to the tedious, manual collection of property data.
3
http://www.statsbygg.no/Om-Statsbygg/About-Statsbygg/
2 Approach and Implementation
Data Sources. Cross-domain government data from different open and propri-
etary sources as listed below are involved in the Linked Data generation process.
– The central government organization dataset (a subset of data from the Nor-
wegian Business Entity Register administrated by the Brønnøysund Register
Centre4 );
– The cadastral dataset (a subset of data from the Norwegian Cadastral Sys-
tem5 administrated by the Norwegian Mapping Authority);
– The building accessibility dataset from the Building Accessibility Register6
administrated by Statsbygg;
– The previous SoE report7 dataset administrated by Statsbygg;
– The municipality boundaries dataset8 administrated by the Norwegian Map-
ping Authority.
The non-geospatial datasets are prepared by dataset providers in tabular format
and the geospatial datasets are provided as shape files.
Though the source data comes from the most authoritative sources in the
respective domains, it is not always complete and accurate. Data inconsistency
between source systems is one of the main challenges in the integration process.
Missing values and keys also represent significant barriers for the integration
process. Such issues are addressed in the Linked Data generation process.
Ontology description. Property related data are rich in attributes, with both
spatial and temporal characteristics. A common ontology model provides the
necessary semantic description of the data. We reuse standard and established
ontologies such as DBpedia-owl, DUL, GeoSPARQL and schema.org to repre-
sent the data. For example, schema:leiCode, schema:legalName, dbpedia-owl:type,
schema:foundingDate and schema:parentOrgnization are used to model organi-
zation number, name, type, founding date and parent organization, respectively,
in the central government organization dataset.
In addition, we developed the proDataMarket9 [3] ontology to model, among
others, the cadastral and building accessibility domain based on existing on-
tologies and standards. For the cadastral domain, the proDataMarket ontol-
ogy reuses the Land Administration Domain Model (LADM) defined in ISO
19152:201210 standard and cadastral parcel concept specified by the European
Union’s INSPIRE data specifications11 .
4
https://www.brreg.no/home/
5
http://www.kartverket.no/en/Land-Registry-and-Cadestre/
6
https://byggforalle.no/uu/sok.html?&locale=en
7
https://www.regjeringen.no/contentassets/f4346335264c4f8495bc559482428908/
no/sved/stateigedom.pdf
8
http://data.kartverket.no/download/content/geodataprodukter
9
http://vocabs.datagraft.net/proDataMarket/
10
http://www.iso.org/iso/catalogue_detail.htm?csnumber=51206
11
http://inspire.ec.europa.eu/data-model/approved/r4618-ir/html/
Linked Data Generation and Publication. The publication of SoE data as
Linked Data was performed with the help of DataGraft [1,2]12 – a cloud-based
platform for data cleaning and Linked Data generation. DataGraft facilitates
interactive data cleaning and transformation, mapping data to Linked Data on-
tologies, generating a semantic RDF graph and provisioning data both as RDF
dump and as a SPARQL endpoint.
Data cleaning and preparation activities for SoE data included assigning valid
cadastral parcel identifiers, unifying null values for attributes with null values,
changing decimal formatting and geospatial data conversion. After the source
data files were cleaned, they were mapped to the above mentioned ontology and
published in DataGraft. Next, the data augmentation was performed with the
help of SPARQL CONSTRUCT queries executed on the published data, thus
making it possible to infer new data based on known business rules. One example
of such a business rule states that a building is owned by the owner or lessor
of a cadastral parcel where the building is built upon. This helped to select out
the state-owned properties and buildings, calculate the area of real estates, infer
the building ownership based on the information about owner or lessor of the
belonging cadastral parcel, etc. In addition, the published data is interlinked
with several central LOD datasets (DBpedia13 , GeoNames14 and Lenka.no15 ) in
order to increase its re-usability to support queries on cross-domain distributed
datasets.
The result of the data augmentation and interlinking process is published on
DataGraft and is available through a SPARQL endpoint16 under the Norwegian
License of Open Data17 (NLOD). SPARQL queries can be run on the SPARQL
endpoint to assess the data quality of the SoE report dataset.
3 Demonstration
During the demonstration, we will present the process of generating Linked Data
from the SoE report using DataGraft, and a web-based application for registra-
tion and reporting state-owned properties in Norway. The demo scenario will
cover uploading raw SoE data to the DataGraft platform, data transformation
and publication as a Linked Data, and the the Web-based application. The appli-
cation allows users to configure visualizations and browse state-owned properties
and relevant data on the map created in CartoDB18 (see Figure 1). The state-
owned properties absent in the previous SoE report are explicitly marked on the
12
https://datagraft.io/
13
http://wiki.dbpedia.org/wiktionary-rdf-extraction
14
http://www.geonames.org/
15
http://data.lenka.no/
16
https://datagraft.io/prodatamarket_publisher/sparql_endpoints/
norwegian-state-of-estate-report-04693e1f-4060-48c1-8ab9-888a6c95f6d6 -
SPARQL querying at this endpoint currently works only in Chrome.
17
https://data.norge.no/nlod/en/1.0
18
https://carto.com/
map (using pink colour), helping users to identify data quality issues such as
inconsistencies or missing registrations in the source systems.
Fig. 1. Visualization of state-owned properties data.
Acknowledgements This work is partly funded by the EC H2020 project proData-
Market (Grant number: 644497).
References
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