=Paper=
{{Paper
|id=Vol-2456/paper14
|storemode=property
|title=An Overview of the TBFY Knowledge Graph for Public Procurement
|pdfUrl=https://ceur-ws.org/Vol-2456/paper14.pdf
|volume=Vol-2456
|authors=Ahmet Soylu,Brian Elvesæter,Philip Turk,Dumitru Roman,Oscar Corcho,Elena Simperl,Ian Makgill,Chris Taggart,Marko Grobelnik,Till C. Lech
|dblpUrl=https://dblp.org/rec/conf/semweb/SoyluETRCSMTGL19
}}
==An Overview of the TBFY Knowledge Graph for Public Procurement==
https://ceur-ws.org/Vol-2456/paper14.pdf
An Overview of the TBFY Knowledge Graph for
Public Procurement?
Ahmet Soylu1 , Brian Elvesæter1 , Philip Turk1 , Dumitru Roman1 , Oscar
Corcho2 , Elena Simperl3 , Ian Makgill4 , Chris Taggart5 , Marko Grobelnik6 , and
Till C. Lech1
1
SINTEF Digital, Oslo, Norway
2
Universidad Politécnica de Madrid, Madrid, Spain
3
University of Southampton, Southampton, the UK
4
OpenOpps Ltd, London, the UK
5
OpenCorporates Ltd, London, the UK
6
Jožef Stefan Institute, Ljubljana, Slovenia
Abstract. A growing amount of public procurement data is being made
available in the EU for the purpose of improving the effectiveness, effi-
ciency, transparency, and accountability of government spending. However,
there is a large heterogeneity, due to the lack of common data formats and
models. To this end, we developed an ontology network for representing
and linking tender and company data and ingested relevant data from
two prominent data providers into a knowledge graph, called TBFY. In
this poster paper, we present an overview of our knowledge graph.
Keywords: Public procurement · Knowledge graph · Ontology.
1 Introduction
In the EU, public authorities spend around 14% of GDP on the purchase of
services, works, and supplies every year7 . Therefore, a growing amount of public
procurement data is being made available in the EU through public portals for the
purpose of improving the effectiveness, efficiency, transparency, and accountability
of government spending. However, there is a large heterogeneity, due to the lack
of common data formats and models for exposing such data.
There are various standardization initiatives for electronic procurement, such
as Open Contracting Data Standard (OCDS)8 and TED eSenders 9 . However,
these are mostly oriented to achieve interoperability, document-oriented, and
provide no standardised practices to refer to third parties, companies participating
in the process, etc. This again generates a lot of heterogeneity. The Semantic Web
?
Copyright c 2019 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
7
https://ec.europa.eu/growth/single-market/public-procurement_en
8
http://standard.open-contracting.org/latest/en/
9
http://simap.ted.europa.eu/
approach has been proposed as a response [1]. For example, several ontologies have
been developed, such as PPROC ontology [3] for describing public processes and
contracts, LOTED2 ontology [2] for public procurement notices, PCO ontology [4]
for contracts in public domain, and MOLDEAS ontology [5] for announcements
about public tenders. Each of these was developed with different concerns in
mind (legal, process-oriented, etc.) without significant adoption so far.
To this end, we developed an ontology network for representing and linking
tender and company data and ingested relevant data from two prominent data
providers into a knowledge graph, called TBFY. In this poster paper, we present
an overview of our knowledge graph for public procurement.
2 Knowledge Graph
We integrated two datasets according to an ontology network: tender data
provided by OpenOpps10 in the OCDS format and company data provided by
OpenCorporates11 . OpenOpps has gathered over 2M tender documents from
more than 300 publishers through Web scrapping and by using open APIs, while
OpenCorporates currently has 140M entities collected from national registers.
2.1 Ontology Network
We are currently using two main ontologies. First, an ontology for tender data
(see Figure 1) that we developed using the OCDS’ data model12 .
Fig. 1. A fragment of OCDS ontology depicting some of the key classes.
Second, we reused the euBG ontology for company data13 . Both ontologies
reuse other ontologies and vocabularies (FOAF, Dublin Core, etc.).
10
https://openopps.com
11
https://opencorporates.com
12
https://github.com/TBFY/ocds-ontology
13
https://github.com/euBusinessGraph/eubg-data
2.2 Data Ingestion
The data ingestion process is composed of several steps using data APIs of both
providers (see Figure 2). Initially, company data is extracted from OpenOpps
for a given period of time and preprocessed primarily to handle null values.
Suppliers appearing in tender data are matched against company data provided
by OpenCorporates by using the reconciliation service of OpenCorporates. The
matched company data is extracted and then supplier data is annotated with
the corresponding company identifiers.
OpenOpps Reconciliation OpenCorporates Knowledge
API API API Graph
3. 5. Ontologies
Fetch
Reconcile company
data
1. 6. 7.
Fetch RML Upload to
tender data Mapping KG
company data
2. 4.
Preprocess Annotate
tender data
Fig. 2. Data ingestion process.
Finally, the extracted datasets are translated to RDF using the RDF Mapping
Language (RML)14 according to our ontology network. The supplier data and
company data is linked through the owl:sameAs property. Thereafter data is
uploaded to a graph database, namely GraphDB15 .
2.3 Current Release
The current release of the knowledge graph includes 23M triples originating from
tender data collected initially for the first quarter of 2019. The knowledge graph
is available online16 . An example query and its results are depicted in Figure 3.
The example query lists top ten companies in the Norwegian jurisdiction that
have the highest number of supplier role, where the jurisdiction data comes from
OpenCorporates and contract data comes from OpenOpps.
3 Open Issues
Currently, one of the main issues concerns data quality including missing, du-
plicate, poorly formed, and erroneous data. For example, the missing address
14
http://rml.io/
15
http://graphdb.ontotext.com/
16
http://data.tbfy.eu
Fig. 3. An example query executed on the TBFY knowledge graph.
information, variations on address format, etc. hinder the quality of reconciliation
process. Currently, we are working on various approaches to improve data quality
ranging from machine learning to crowd-sourcing.
Acknowledgements. This work has been partly funded by the EU H2020
projects TheyBuyForYou (780247) and euBusinessGraph (732003).
References
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technologies: Current status and future challenges. Computers in Industry 65(5),
800–820 (2014)
2. Distinto, I., et al.: LOTED2: An ontology of European public procurement notices.
Semantic Web 7(3), 267–293 (2016)
3. Muñoz-Soro, J.F., et al.: PPROC, an ontology for transparency in public procurement.
Semantic Web 7(3), 295–309 (2016)
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Industry 65(5), 862–877 (2014)
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