=Paper=
{{Paper
|id=Vol-2198/paper_126
|storemode=property
|title=A Semantic Catalogue for the Data Market Austria
|pdfUrl=https://ceur-ws.org/Vol-2198/paper_126.pdf
|volume=Vol-2198
|authors=Bernd-Peter Ivanschitz,Thomas J. Lampoltshammer, Victor Mireles,Artem Revenko,Sven Schlarb,Lőrinc Thurnay
|dblpUrl=https://dblp.org/rec/conf/i-semantics/IvanschitzLMRST18
}}
==A Semantic Catalogue for the Data Market Austria==
https://ceur-ws.org/Vol-2198/paper_126.pdf
A Semantic Catalogue for the Data Market
Austria
Bernd-Peter Ivanschitz1 , Thomas J. Lampoltshammer2 , Victor Mireles3 ,
Artem Revenko3 , Sven Schlarb4 , and Lőrinc Thurnay2
1
Research Studios Austria, Thurngasse 8/16, Vienna, Austria
bernd.ivanschitz@researchstudio.at
2
Danube University Krems, Dr.-Karl-Dorrek-Str. 30, Krems an der Donau, Austria,
3
Semantic Web Company, Neubaugasse 1, Vienna, Austria
4
AIT Austrian Institute of Technology, Giefinggasse 4, Vienna, Austria
Abstract. The Data Market Austria (DMA) is an ecosystem of fed-
erated data and service infrastructures. It aims at making data from
various data providers accessible and interoperable by allowing the sub-
mission, storage, management and dissemination of static datasets or
streaming data services. By creating a metadata vocabulary, standard-
izing the ingest of data and ensuring the quality and completeness of
metadata, it lays the ground to enable participants to share or consume
datasets residing in different infrastructures. This demo focuses on the
mapping services used in the DMA to standardize data from different
sources using a modified version of the DCAT metadata schema. We
present tools that enable inter organizational integration of datasets, in
a manner that is both user-friendly and powerful enough to handle vast
amounts of data.
Keywords: Metadata mapping, semantic enrichment, RDF,distributed
systems, RML, Metadata catalogue
1 Introduction
The amount of data produced every day is growing at breathtaking speed – data
has become an important asset that is of high importance in nearly every indus-
try sector worldwide [6]. Therefore, a healthy data economy and a successfully
functioning data-services ecosystem enable and ensure sustainable employment
and growth and thereby societal stability and well-being [4,5]. Several issues have
been identified as hindering the data economy in the Austrian case [2], among
them the lack of interconnection between different infrastructures hosting data
and data related services.
The Data Market Austria (DMA)5 project addresses these problems by de-
veloping the technological, infrastructural, regulatory, and economic founda-
tions for a comprehensive, innovation-supporting, sustainable Austrian data-
services ecosystem. The technological foundation includes Blockchain technology
5
https://datamarket.at/
2 Ivanschitz et al.
for provenance, smart contracts and security, interconnected clouds, data access,
constraint-preserving processing and analysis algorithms, semi-automated data
quality improvement, and recommender-based brokerage technology. Addition-
ally, two pilots in the areas of ICT for Mobility and ICT for Earth Observation
are being developed to demonstrate the first usage scenarios of DMA.
The DMA is a network of participating (or member) organizations that con-
tribute to the data market by offering their products in form of datasets or
services to customers of the DMA. Each participating node must implement a
defined set of services and mandatory standard interfaces. These are, for ex-
ample instances of a Data Crawler a Metadata Mapper, a Blockchain peer, and
Data Management and Storage components. Together with a common concep-
tual model, these standard interfaces represent the basis of interoperability for
the use of datasets in the DMA.
The gateway to this network of nodes containing data and providing services
is the DMA portal which, while not hosting any data or providing major services,
collects information from all nodes to keep an up to date catalogue of available
datasets. The focus of this demo is the design and implementation of this unified
catalogue.
2 A Semantic Catalogue for a Data Market
Since the data in the DMA lies in a set of distributed repositories, it is necessary
to build a unified catalogue to enable end users to search all available data sets
and services. Furthermore, a single catalogue can be exploited for recommenda-
tion, deduplication, and various metadata quality measures. In the DMA, the
creation of this unified catalogue is approached by creating i) a single metadata
standard for unified representation of data sets, including standardized vocabu-
laries for describing resources, ii) tools for facilitating the compliance of existing
metadata with the previous points and iii) the technological foundation for the
building and maintenance of the catalogue itself.
Metadata standard
The DMA metadata catalogue is based on DCAT-AP, the DCAT application
profile for data portals in Europe6 and extends the schema for DMA use cases.
This standardization enables future cooperation with international data portals
and ensures that the DMA is easily accessible for cooperating companies with
a certain data quality standard. The DMA extension of the DCAT-AP, the
Data Maket Core Vocabulary (DMAV), provides more classes and properties for
describing datasets and services that are accessible on the DMA. The extension
focuses on the business use case of the DMA and adds predicates covering topics
like price modeling and dataset exchange, not present in the original DCAT-
AP catalogue. The dmav:priceModel predicate, for example, allows us to handle
the transaction fees for commercial datasets that are being made available in
6
https://joinup.ec.europa.eu/release/dcat-ap-v11
Semantic catalogue for the Data Market Austria 3
the DMA. The dmav:SLA (Service Level Agreement) class allows to model the
condition of a service contract in more details.
In the DMA metadata catalogue, every dataset constitutes an RDF7 resource.
There is a set of predicates that link every resource to different literals, which
constitute the values of the metadata fields. These values can be of two types:
i) literals, as in the case of dcat:description or owl:versionInfo, or ii) elements of
a controlled vocabulary, as in the case of Language or License. These controlled
vocabularies, which are managed by PoolParty Semantic Suite8 , enable accu-
rate search, filtering and linking of different datasets. Additionally, the DMA
includes a series of semantic enrichment services which automatically annotate
free-text fields (such as dcat:description or dcat:title) with elements of controlled
vocabularies.
Tools for adoption of the metadata standards
Since the DMA aims at making available data which was not originally produced
for commercialization, we must assume that the metadata describing it does not
comply to any particular standard. This is specially true because the data in
each node is managed by a different organization. Therefore, the conversion to
the unified metadata standard described above must be treated in a case by case
basis.
The DMA provides two tools to facilitate this. The first is a UI component
in which a node’s administrator can upload a sample (in XML or JSON) of the
metadata they wish to make abailable in the DMA. They are then prompted to
select, for each of the metadata fields required by the DMA, which fields of their
metadata schema should be used. This UI tool, called the Metadata Mapping
Builder is, in essence, a user-friendly way to generate XPath and JSONPath
expressions. Once these expressions have been generated, they are arranged into
an RML[1] file, which is then used to produce RDF from similarly structured
XML or JSON files.
Catalogue compilation and maintenance
Each node in the DMA that wishes to make a series of datasets available, must
implement the following workflow. First, the Data Harvesting Component, which
must be configured by the node’s administrator to find the different datasets
within the node, sends the corresponding metadata files to the Metadata Map-
ping Service, which uses the mapping file created as described above to generate,
for each dataset, a set of RDF triples (serialized in Turtle format).
Afterwards, the dataset, its original metadata, and the corresponding RDF
are ingested into the Data Management component which takes care of the
packaging, versioning and assignment of unique identifiers to all datasets, whose
7
https://www.w3.org/RDF/
8
https://www.poolparty.biz/
4 Ivanschitz et al.
hashes are furthermore registered in the Blockchain. Next The node’s Data Man-
agement component publishes, through a ResourceSync9 interface, links to meta-
data files in RDF format of recently added or updated datasets. This way, the
node’s metadata management is decoupled from the process of incorporating
metadata into the DMA catalogue.
In the DMA’s central node, the Metadata Ingestion component constantly
polls the ResourceSync interfaces of all registered nodes, and when new datasets
are reported, harvests their RDF metadata which, let us recall, already complies
with the DMA metadata vocabulary. This metadata is then enriched semanti-
cally. The enrichment is based on EuroVoc10 , which is used in DMA as the main
thesaurus. The NLP interchange format [3] is used for annotations, which are
done in stand-off mode. The mapped and enriched metadata is then ingested
into the Search and Recommendation Services. The high quality of the meta-
data and its compliance to the chosen scheme guarantees that the datasets and
service are discoverable by the users of DMA.
With small variations, the processes described above are also used for in-
gesting publicly available data from goverment portals as well as ingesting small
amounts of data that an individual would like to make available in the DMA.
Acknowledgements The Data Market Austria project is funded by the “ICT
of the Future” program of the Austrian Research Promotion Agency (FFG) and
the Federal Ministry of Transport, Innovation and Technology (BMVIT) under
grant no. 855404
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