=Paper= {{Paper |id=Vol-2489/paper1 |storemode=property |title=Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences |pdfUrl=https://ceur-ws.org/Vol-2489/paper1.pdf |volume=Vol-2489 |authors=Lars Heling,Felix Bensmann,Benjamin Zapilko,Maribel Acosta,York Sure-Vetter |dblpUrl=https://dblp.org/rec/conf/esws/HelingBZAS19a }} ==Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences== https://ceur-ws.org/Vol-2489/paper1.pdf

Building Knowledge Graphs from Survey Data:
A Use Case in the Social Sciences

Lars Heling1 , Felix Bensmann2 , Benjamin Zapilko2 ,
Maribel Acosta1 , and York Sure-Vetter1
1
Institute AIFB, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
firstname.lastname@kit.edu
2
GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany
firstname.lastname@gesis.org

Abstract. Many research endeavors in the social sciences rely on high-
quality empirical data. Survey data is often used to investigate social and
political behavior. The GESIS Panel is a probability-based mixed-mode
panel survey in Germany providing high-quality survey and statistical
data about e.g. political opinions, well-being, and other contemporary
societal topics. In general, the process for integrating and analyzing the
relevant data is very time-consuming for researchers. This is due to the
fact, that search, discovery, and retrieval of the survey data require ac-
cessing various data sources providing diﬀerent information in diﬀerent
file formats. In this paper, we present our architecture for building a
Knowledge Graph of the GESIS Panel data. We present the relevant
heterogeneous data sources and demonstrate how we semantically lift
and interlink the data in a shared RDF model. At the core of our archi-
tecture is the Knowledge Graph representing all aspects of the surveys.
It is generated in a modular fashion and therefore, our solution can be
transferred to the existing infrastructure of other survey data publishers.

Keywords: Knowledge Graph, Survey Data, RDF, DDI

1 Introduction and Motivation
Linked Open Data initiatives have led to an increasing amount of data being
published using the Resource Description Framework (RDF) on the web. At the
core of RDF is the concept of linking resources within or across RDF graphs
such that the resulting dataspace can be understood as a Knowledge Graph
(KG) [7]. This allows data publishers to independently administer and publish
their own data and improving its value and visibility by linking it to data of
other publishers oﬀering similar or additional information on the resources. In
this paper, we present an in-use application of such a KG in the domain of the
social sciences at GESIS - Leibniz Institute for the Social Sciences. Our work
is motivated by the circumstance that data related to the GESIS Panel3 like
Copyright ©c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
3
https://www.gesis.org/en/gesis-panel/gesis-panel-home/
2 Heling, Bensmann, Zapilko, Acosta, Sure-Vetter

Keyword-based Potential Codebook Relevant Analysis

+ +
Search Studies PDFs Variables Dataset

Hypothesis

Survey Participant
Metadata Observations

Fig. 1: Motivation: Current process to retrieve survey data based on a hypothesis.

questionnaires and observation data is administered and published in diﬀerent
datasets varying in format and representation. As a result, the current process
for researchers aiming to use the rich collection of surveys available at GESIS
requires manually consulting diﬀerent information sources to discover and obtain
relevant data which is a time-consuming task.
Motivating Scenario. Consider the current process to discover and retrieve
the data from the GESIS Panel outlined in Figure 1. A researcher has a research
question and formulates a hypothesis according to which she aims to investigate
by leveraging the data provided by the GESIS Panel. Typically, the researcher
first starts to discover the available survey datasets by a keyword-based search
in the Data Catalog (DBK)4 , which is the online portal to search and retrieve
survey related data. The search results are a list of surveys which match the
keyword on the survey-level metadata, e.g. in the abstract summarizing the
survey. Based on this list, the researcher can retrieve the codebook PDFs for all
surveys from the portal. In the codebooks, the variables assessed in the surveys
are detailed, and the researcher may search for all relevant variables. To obtain
the final analysis dataset, the researcher needs to access the CSV documents with
the recorded participant answers (or observations) for the relevant variables. In
some cases, a download from the DBK is not available because of data protection
laws and researchers are required to physically visit the Secure Data Center Safe
Room at GESIS to access and work with the data on-site. After retrieving the
final dataset, the researcher may use statistical analysis tools to investigate the
hypothesis. This tedious process from a hypothesis to gaining first insights into
the actual data impedes the research process for social scientists.
The goal of building a KG for the survey data is improving this process for
researchers by facilitating the discovery and retrieval of relevant data. Using
Semantic Web technologies as a foundation allows for publishing and linking
data of independent sources providing a holistic picture of the GESIS Panel in
the form a KG. Therefore, the contributions of this work are the following:
C1 Description and analysis of a real world scenario from the social sciences
domain with corresponding requirements,
C2 Outline of our solution to handle data organization requirements by ap-
plying Semantic Web technologies to create a Knowledge Graph, and
C3 Presentation of encountered challenges, lessons learned, and indication
of future extensions.
4
https://dbk.gesis.org/dbksearch/
Building Knowledge Graphs from Survey Data 3

In addition, we provide a demo5 allowing access to parts of the KG. The re-
mainder of this paper is structured as follows. In Section 2, we provide the
preliminaries by introducing the GESIS Panel and relevant vocabularies, i.e.,
the DDI and the DDI-RDF Discovery Vocabulary. In Section 3, we present the
architecture of our approach. We then revisit our motivating scenario and outline
challenges encountered and lessons learned in Section 4 and analyze related work
in Section 5. We summarize our work in Section 6 and indicate future works.

2 Preliminaries

In the following, we introduce the GESIS Panel, the Data Documentation Ini-
tiative (DDI) and the corresponding DDI-RDF Discovery Vocabulary.

2.1 GESIS Panel

The GESIS Panel3 is a probability-based mixed-mode panel survey in Ger-
many which is open to the research community [3]. The goal is obtaining high-
quality survey data by employing a cross-sectional or longitudinal survey design.
Probability-based indicates a participant selection optimized to accurately esti-
mating the target population, which are German-speaking persons between age
18 and 70 who live in private households in Germany. Mixed-mode refers to the
two modes of the data collection process, namely via web-based surveys or via
traditional paper-and-pencil surveys sent to the participants. The data collection
is performed periodically in waves on a bimonthly basis with a new questionnaire
in each period, producing a continuously growing dataset. The data is published
in three editions: standard edition, extended edition and campus file, each cover-
ing diﬀerent subsets of the recorded data. Standard edition and campus file can
be retrieved online, while the extended edition may only be accessed within the
aforementioned Safe Room. The data collected in the GESIS Panel may serve as
a basis for analyses in the social sciences and it has been used in several studies,
for example, to examine the political opinions of the German population [4,6].

2.2 DDI and DDI-RDF Discovery Vocabulary

The Data Documentation Initiative (DDI)6 is an internationally acknowledged
standard to facilitate data management by documenting metadata on the datasets
in the area of social, behavioral and economic sciences [9]. Therefore, the stan-
dard aims to improve data quality and ensure the long-term preservation of the
information and it is driven by an alliance of data producers, archivists and users
to jointly collaborate on the standard [9]. The DDI-RDF Discovery Vocabulary7
(disco) aims at transferring the DDI standard to the Linked Data community.

5
https://km.aifb.kit.edu/services/gesispanel/demo
6
https://www.ddialliance.org
7
http://rdf-vocabulary.ddialliance.org/discovery.html
4 Heling, Bensmann, Zapilko, Acosta, Sure-Vetter

It is based on a subset of DDI allowing for describing survey data in the social
sciences which facilitates the discovery of this data and related metadata [1,2].
At the core of the vocabulary is the Study class which represents the genera-
tion process of a dataset. A set of studies is compiled in a StudyGroup in case
the surveys are conducted in a continuous or periodic process. For example, each
wave of the GESIS Panel can be modeled as a Study and they are combined into
one StudyGroup. The content of the physical dataset holding the actual original
survey data is represented in a LogicalDataSet for which licensing information
and access policies may be attached. The content of a dataset is described by
Variables. Variables represent diﬀerent aspects which are measured as part of a
Study and, thus, are typically the columns in a tabular representation of the sur-
vey records. The data of a survey is commonly collected using a Questionnaire
which consists of a set of Questions to measure the variables. Variables are as-
sociated with a Representation which is typically the set of answers for the
associated question and the corresponding notation used in the dataset. The
Representation is linked as the responseDomain to a question. Furthermore,
the target population of a Study may be described using the classes Universe
and AnalysisUnit. For instance, the target population of the GESIS Panel is a
representative sample of the German population and, thus, the analysis unit is
persons. The development of an RDF vocabulary along with the already existing
DDI standard is motivated by various use cases which mostly support the dis-
coverability of the data [1,11]. For instance, free text keyword-based search may
be enabled and once studies and relevant data has been found, related studies
and additional data may be discovered exploiting the links across the datasets.

3 Building a Knowledge Graph for the GESIS Panel

The goal of building a Knowledge Graph (KG) for the GESIS Panel by semanti-
cally lifting the original data sources to a shared RDF data model is improving
the discovery, search and retrieval of survey data for social scientists. In the fol-
lowing, we provide an overview of the architecture and thereafter, describe the
original data sources as well as the semantic lifting process in more detail.

3.1 Architecture

Figure 2 provides an overview of our architecture and the main components.
From an integration perspective, the integration process is visualized in a bottom-
up manner. At the bottom are the data sources providing diﬀerent parts of data
associated with the GESIS Panel:i) the access right management data associ-
ated with the datasets, ii) the survey metadata providing general information
about surveys and corresponding waves, iii) the codebooks with information
on how the variables in a survey are to be interpreted, and iv) the participant
observations (unit-records) which encode the respondents’ answers to the ques-
tionnaires. The data sources vary in format and schema. Therefore, each data
source requires a custom semantic lifting process to transfer the original data to
Building Knowledge Graphs from Survey Data 5

Federated Query Engine
Integration
SPARQL SPARQL SPARQL SPARQL Layer

Access Graph GESIS Panel Knowledge Graph

Survey Data
RDF Graphs

Semantic
Lifting Process

Original
Survey
i) ii) iii) iv)
Access Right Survey Questionnaire Participant Data
Management Metadata Codebook Observations

Fig. 2: Architecture of the infrastructure for building the GESIS Panel KG.

the shared RDF data model. Since the data sources are heterogeneous in nature
and their maintenance is deeply rooted in and grown together with the organi-
zation of GESIS, the lifting processes need to be invoked individually, whenever
updates on a specific dataset are to be made. Each semantic lifting process takes
an original data set as input and returns an RDF graph. By defining conven-
tions for naming resources (URIs) of common instances across the diﬀerent data
sources, they are interlinked across the RDF graphs. As a result, each graph
stands for itself but combined together they provide a holistic KG of the GESIS
Panel. In our implementation, each graph is provided via an individual SPARQL
endpoint as this allows for the original data providers to independently manage
and publish their data. Furthermore, survey metadata and codebook data may
be oﬀered via public endpoints to allow researchers to discover available data
while parts of the participant observations may only be accessed within the Safe
Room to comply to data security and privacy regulations of GESIS. Finally, at
the top is the integration layer consisting of SPARQL endpoints which may be
accessed by a federated engine to query the KG. The integration layer may be
directly queried by users or, alternatively, accessed by an application such as a
GUI. In our specific case, the non-sensitive data may be merged and provided
by a single endpoint for performance improvements when querying the data.
Here we present the generic architecture as this may not be applicable in any
organization. According to this bottom-up architecture and existing processes
at GESIS, changes in individual data sources are propagated from the original
data to the KG via a semantic lifting process.

3.2 GESIS Panel Knowledge Graph

We provide a simplified example extract of the GESIS Panel KG in Figure 3
to exemplifying the RDF graphs from the diﬀerent data sources and how they
are interlinked. Thereafter, we detail the semantic lifting process. Starting at
6 Heling, Bensmann, Zapilko, Acosta, Sure-Vetter

the top, Figure 3a shows user User1 and a RightStatement to indicate the
user’s permissions to access the extended edition of GESIS Panel data. This
RightStatement is linked to the metadata for the survey WaveA as shown in
Figure 3b. The figure shows a subset of the original metadata that includes the
LogicalDataSet providing the data, title, subjects, variables of the wave, as well
as the time period in which the wave was conducted. In the example, merely the
variable Gender is shown for the wave. The variable is linked to the questionnaire
codebook subgraph shown in Figure 3c which provides details for the variable
such as the question text and corresponding answers as well as their notation.
Moreover, each variable has a corresponding property which is used in the partic-
ipant observation subgraph. Figure 3d shows the recorded data for a participant
represented using the RDF Data Cube Vocabulary8 . Each observation is a blank
node linked to the participant’s identifier and the recorded variable values using
the corresponding properties. In the example, the participant is male according
to the notation provided in the questionnaire codebook.

3.3 Original Data and Semantic Lifting

In the following, we describe the original data available at GESIS and detail how
the data is semantically lifted to the shared RDF data model of our KG.
Access Right Management. Considering the access policies, there are three
diﬀerent editions of the GESIS Panel: campus file, standard edition, and ex-
tended edition. In each edition, a diﬀerent subset of survey data and variables
are available. Accordingly, the access rights need to be defined on this level. The
Dublin Core vocabulary (dcterms), which is reused in the disco vocabulary,
allows for defining such access right statements on the level LogicalDataSets
and the data is associated with the corresponding access rights according to the
edition. Furthermore, a user model is employed to define users and link them to
the access right statements. Currently, this process is implemented in a manual
fashion, however, we aim to integrate the access right management for our KG
to existing solutions, such as the Lighweight Direcory Access Protocol (LDAP).
Survey Metadata. The Data Catalog (DBK)4 is the online portal provided by
GESIS to search and retrieve survey data including the GESIS Panel. The DBK
operates on metadata describing surveys as a whole but not on the level of in-
dividual variables. Important aspects in the metadata are, for instance, citation
data, version information, date of collection, or methodology. The survey-level
metadata may also be retrieved from an internal database as XML documents
following the DDI standard, where the data is continuously updated in an auto-
matic fashion. As the GESIS Panel is considered as a single evergrowing survey,
it is represented in a single large DDI file comprising the information of all as-
sociated waves. However, each time a wave is added, a new version is created
for researchers to keep track of data provenance. We choose to represent the
survey on the level of waves as individual Studys to allow for a consistent and
retraceable mapping to the corresponding concepts of the disco vocabulary.
8
https://www.w3.org/TR/vocab-data-cube/
Building Knowledge Graphs from Survey Data 7

(a) Access Right Management
dcterms:Agent
rdf:type

foaf:lastname
agent:User1 ''Doe''@en
dcterms:rightsHolder
foaf:firstname
rdf:type
dataset:ExtendedEditionAccessRights dcterms:RightStatement ''John''@en
dcterms:description

''Access to the Gesis Panel Extended Edition.''@en

(b) Survey Metadata

dataset:WaveA dcterms:accessRight dataset:ExtendedEditionAccessRights
disco:product

''Wave A''@en
dcterms:title
disco:startDate ''2017-04-19''^^xsd:date

dcterms:temporal period:WaveA disco:endDate
survey:WaveA ''2017-06-13''^^xsd:date
rdf:type
disco:Study
dcterms:subject

disco:variable skos:prefLabel
subject:WaveA-44 ''Political Interest''@en

variable:Gender

skos:notation ''1''^^xsd:integer
answer:Male
skos:inScheme

skos:notation ''0''^^xsd:integer
answer:Female
skos:inScheme

disco:representation rep:Gender
disco:responseDomain

disco:question question:Gender disco:questionText
''Gender of respondent''@en
variable:Gender skos:prefLabel skos:inScheme

''Gender''@en
sora:hasProperty

answer:NotInited skos:notation ''-1''^^xsd:integer

prop:gender rdf:type rdf:Property

(d) Participant Observation

''1''^^xsd:integer
prop:gender

rdf:type qb:Observation
_:observation1 prop:internetUsage
sora:participant ''6''^^xsd:integer

participants:123456

Fig. 3: Knowledge Graph Extract: The figures visualize the subgraphs of the
Knowledge Graph to provide an overview of the shared RDF data model and
the interlinking between the data sources. The dashed arrows indicate these re-
lationships between shared resources. (The prefixes for dcterms, disco, foaf,
rdf, skos and qb are used as in prefix.cc. The sora prefix is used for
our vocabulary9 . The other prefixes adhere to the scheme http://.../gesis/
resource//.)
8 Heling, Bensmann, Zapilko, Acosta, Sure-Vetter

Table 1: Questionnaire Codebook: Simplified example extract of the codebook
for a variable assessing a participant’s gender measured in two surveys.

varname labelEn questionText code valueLabel waveID betweenCorrespondence . . .
gender Gender Gender of the respondent -1 Not Inited WaveA genderB ...
gender Gender Gender of the respondent 0 Female WaveA genderB ...
gender Gender Gender of the respondent 1 Male WaveA genderB ...
... ... ... ... ... ... ... ...
genderB Gender Gender of the respondent -1 Not Inited WaveB ...
genderB Gender Gender of the respondent 0 Female WaveB ...
genderB Gender Gender of the respondent 1 Male WaveB ...
... ... ... ... ... ... ... ...

Questionnaire Codebook. The detailed information on the variables and the
corresponding questionnaires for each wave are provided in codebooks. For re-
searchers, the codebooks are accessible as PDF files in the DBK portal. Inter-
nally, these PDFs are generated from CSV files containing all the necessary
information in a tabular format. The data for the example extract is shown in
Table 1. Variables are uniquely identified by a varname which is based on the
identifier of the corresponding wave (i.e., waveID) and a number (omitted for
visualization purposes). Variables are represented in the rows and depending on
the type of question and set of answers, several rows represent a single variable.
This type of tabular representation requires two major aspects to be considered
in the semantic lifting process:i) information related to variables such as corre-
sponding answers and their notation may be stored in a redundant fashion, and
ii) semantically identical variables are assessed in various waves but diﬀerently
identified in each wave, which requires means of linking them. In order to address
the first aspect, the URI for the representation of a variable is derived from the
URI of the variable itself, e.g. variable:Gender → rep:Gender. The URIs for
answers are based on hashing the German and English answer text in combi-
nation with the notation of the answer. Consequently, reoccurring answers are
identified by existing URIs to avoid redundancies. The second aspect is addressed
by defining properties as part of our own vocabulary9 , prefixed with sora, to link
the reoccurring variables. For instance, sora:betweenCorrespondence links a
variable to a similar variable in another survey. The vocabulary also provides
terms to associate variables with additional information from the original CSV
files, such as introductory texts for the questions.
Participant Observations. The last data source provides the participant ob-
servations, i.e., the answer provided by the participants to the questions of each
survey. The observations are provided in a tabular form with each row corre-
sponding to a participant and the columns to the measured value for the vari-
ables. The column names for the variables are the varname identifiers from the
codebooks. The data is distributed over several CSV files which allows restricting
the access according to the aforementioned editions. The first columns provide
9
https://w3id.org/sora/resource/vocabulary
Building Knowledge Graphs from Survey Data 9

basic metadata, such as the unique personal identifier (PID) of the participants
and the version of the data. In the semantic lifting process, for each row, a data
cube Observation is instantiated as a blank node and it is linked to the re-
spective participant URI which is derived from the PID. This allows linking the
observations for the same participant across several studies. The answers are
added to the observation using the corresponding property. Since the identifiers
varname from the codebooks and the column names in the observation files coin-
cide, the URIs for the properties can be created independently of the codebooks.
For example, column gender → prop:gender.
Semantic Lifting. A major requirement for the semantic lifting process was a
lightweight solution applicable to all data sources to facilitate maintenance and
adoption. We investigated existing mapping languages and integration tools,
coming to the conclusion that due to discrepancies in the structure of some
original data sources and the target DDI RDF data model, there is no single
out-of-the-box solution for our use-case. For instance, as in the DDI XML stan-
dard, the GESIS Panel is considered a single (evergrowing) study, we need to
apply complex regular expressions on the textual study description containing
the information on all waves to generate the RDF data. As a result, the data
cleaning, semantic lifting, and refinement process is currently implemented in
Jupyter notebooks10 using Python with RDFLib11 . In the future, we aim to
further investigate mapping approaches to improve the lifting process with a
solution suitable for both the partially unstructured and statistical data.

4 Challenges and Lessons Learned
In the following, we revisit the motivating scenario and show how the discovery
and retrieval of survey data is facilitated by our solution. Thereafter, we discuss
the challenges we encountered and present our solutions to overcome them.
In Section 1, we outlined the current process for a researcher to follow in
order to search, discover, and retrieve survey data of the GESIS Panel. As our
example shows, the current process requires consulting various data sources in
diﬀerent formats and representations entailing a very time-consuming process for
users. The shortcoming is addressed by our approach as the KG may be queried
via a single interface accessing the data represented in the shared RDF model.
As a result, federated SPARQL queries may be processed over the diﬀerent
data sources which improve both the discovery of relevant data as well as its
retrieval. Moreover, a more fine-grained search leveraging the information of the
questionnaires is supported as this information is represented in our KG. Let
us consider the following example: “Find the questions and surveys where the
variable label or the question text contains the term politic from the Extended
Edition of the GESIS Panel of surveys conducted in 2014”. In the current process,
this would require searching all surveys conducted in 2014 and examining every
corresponding codebook PDF manually for the term politic. In contrast, the
10
https://jupyter.org/
11
https://github.com/RDFLib
10 Heling, Bensmann, Zapilko, Acosta, Sure-Vetter

Listing 1.1: SPARQL query exemplifying a search to retrieve questions and cor-
responding waves related concerning politics.
0 SELECT ?question ?wave WHERE {
1 # Retrieve accessible waves
2 ?wave disco:product [ dcterms:accessRights dataset:ExtendedEditionAccessRights ] .
3 ?wave dcterms:temporal [disco:endDate ?end ; disco:startDate ?start ] .
4 # Filter according to the time period
5 FILTER (?start >=‘‘2014−01−01’’xsd:date && ?end <‘‘2015−01−01’’xsd:date)
6 # Retrieve variable label and question text
7 ?wave disco:variable ?variable .
8 ?variable skos:prefLabel ?varLabel .
9 ?variable disco:question [ disco:questionText ?question ].
10 # Filter according to the keyword
11 FILTER (regex(?varLabel, ‘‘politic’’) regex(?question, ‘‘politic’’)) }
required information can be retrieved from our KG using a single SPARQL
query executed by a federated query engine. The query is shown in Listing 1.1.
In lines 2 and 3, the information for the survey are retrieved and filtered in line 5
to include only surveys from 2014. In line 7, all associated variables are selected
and the corresponding label and question texts are selected in lines 8 and 9.
Finally, the results are filtered in line 11 according to the keyword politic.
Several challenges need to be addressed when building the KG which mainly
originate from the organizational structure of GESIS as well as data security and
privacy requirements. The relevant GESIS Panel data is distributed across and
administered in diﬀerent datasets with varying formats and schema, hindering
merging the data in a single repository. We address by defining semantic lifting
processes for each data source to create multiple RDF graphs. By defining con-
ventions for naming resources (i.e., the URIs) the resulting graphs are interlinked
and can be understood as single KG. Whenever a new version of the graph is
created, the data is published via the corresponding SPARQL endpoints such
that a federated query engine may process queries across the entire KG. Further-
more, this semi-automatic and curated semantic lifting process to generate the
RDF graphs were chosen to assure the provision of high-quality data. Sensitive
data may be provided by endpoints only accessible in the network of the Safe
Room at GESIS which addresses the data security and privacy requirements.
Concluding, the major lesson learned was embracing the existing infrastruc-
tures at the organization and develop the architecture accordingly. This will lead
to a sustainable solution which is easier to maintain in the future. Moreover, we
learned that the disco vocabulary is suitable for our use case as it covers almost
all relevant aspects of our KG. Defining additional terms to cover all aspects
specific to the data publisher is inevitable.

5 Related Work

The application of Semantic Web technologies and publishing data according
to the Linked Data principles have been studied in the area of social sciences.
Building Knowledge Graphs from Survey Data 11

The motivation of most research endeavors is improving the discovery and use of
survey and statistical data by publishing metadata and facilitating integration.
Similar to our work, Gottron et al. [5] address the problem of survey and
statistical data being scattered across various files and data sources. They pro-
pose the use of open semantic models to facilitate the searching, merging and
aggregating such distributed data. They present the Semantic Digital Library of
Linked Data, a framework tailored for the social sciences. The authors mainly
focus on integrating and merging data from two data sources, survey and statis-
tical data, on an aggregation level using the Data Cube vocabulary8 . In contrast
to our work, they do not include search capabilities in their prototype but mostly
focus on visualization and lightweight calculations over the integrated data.
Zapilko et. al [10] present a solution to provide a linked thesaurus for the
social sciences (TheSoz), which is essential for indexing documents and research
information in the social sciences, such as survey descriptions. The original the-
saurus is administered in a database and the authors present their approach
transforming the thesaurus to Linked Data using the Simple Knowledge Organi-
zation System (SKOS)12 standard. The presented thesaurus allows for connect-
ing heterogeneous datasets and therefore, may be linked to our KG to further
improve the discovery of survey data by leveraging annotations using TheSoz.
Similarly, Schaible et al. [8] aim to interlink study descriptions to the Linked
Open Data Cloud. They also transform DDI XML documents providing the
study-level descriptions to RDF in order to link the resulting dataset to re-
sources from the Integrated Name Authority File (GND) and DBpedia. For the
linking task, they use Silk13 to discover and generate owl:sameAs links between
a source dataset and target datasets. In contrast to our work, the authors focus
on the study-level metadata and do not provide a holistic approach spanning all
relevant data sources and the associated intricacy, such as privacy requirements.
Furthermore, their primary goal was linking resources from the description to
other resources in the LOD Cloud. As a result, their approach may be applied as
a subsequent refinement step after the KG is created and may be extended to the
semantic data about variables and questionnaires produced with our solution.

6 Conclusions
In this work, we presented our solution to build a Knowledge Graph (KG) for
survey data to facilitate search, discovery, and retrieval of survey data. This
is achieved by semantically lifting data from heterogeneous data sources to a
shared RDF data model based on the DDI-RDF Discovery Vocabulary and pro-
viding the result graphs via SPARQL endpoints to enable the integration using
federated SPARQL query processing while adhering to data security and privacy
requirements. The presented solution overcomes various challenges and require-
ments common to organizations publishing survey data and, therefore, may be
applied in other organizations as well. The described architecture is used in the
12
https://www.w3.org/2004/02/skos/
13
http://silkframework.org/
12 Heling, Bensmann, Zapilko, Acosta, Sure-Vetter

SoRa 14 project which aims to link survey data with geospatial information. In
the future, we aim at extending our current approach by including an additional
refinement step to further enhance the information in the KG. For example, sim-
ilar to [8], NLP may be applied to extract information from the questions text
and survey descriptions to associate them with specific topics or link them to
named entities. In addition, we want to extend the KG to include further surveys
conducted by GESIS. Furthermore, as part of the SoRa project we will apply
our solution at other survey data providers, namely the German Socio-Economic
Panel15 . Ultimately, future work may focus on enabling querying KGs of survey
data across diﬀerent organizations.
Acknowledgments
This work was carried out with the support of the German Research Foundation (DFG)
within the project “Sozial-Raumwissenschaftliche Forschungsdateninfrastruktur”14 .

References
1. Bosch, T., Cyganiak, R., Gregory, A., Wackerow, J.: DDI-RDF discovery vocabu-
lary: A metadata vocabulary for documenting research and survey data. In: LDOW
(2013)
2. Bosch, T., Wackerow, J., Cyganiak, R., Zapilko, B.: Leveraging the DDI Model for
Linked Statistical Data in the p. 10 (2012)
3. Bosnjak, M., Dannwolf, T., Enderle, T., Schaurer, I., Struminskaya, B., Tanner,
A., Weyandt, K.W.: Establishing an Open Probability-Based Mixed-Mode Panel of
the General Population in Germany: The GESIS Panel. Social Science Computer
Review 36(1), 103–115 (Feb 2018)
4. Gherghina, S., Geissel, B.: Citizens’ Conceptions of Democracy And Political Par-
ticipation In Germany. Workshops of European Consortium for Political Research
p. 25 (2015)
5. Gottron, T., Hachenberg, C., Harth, A., Zapilko, B.: Towards a Semantic Data
Library for the Social Sciences p. 13 (2011)
6. Mayer, S.J., Schultze, M.: The eﬀects of political involvement and cross-pressures
on multiple party identifications in multi-party systems – evidence from Germany.
Journal of Elections, Public Opinion and Parties pp. 1–17 (Apr 2018)
7. Paulheim, H.: Knowledge graph refinement: A survey of approaches and evaluation
methods. Semantic web 8(3), 489–508 (2017)
8. Schaible, J., Zapilko, B., Bosch, T., Zenk-Möltgen, W.: Linking Study Descriptions
to the Linked Open Data Cloud. IASSIST Quarterly 38(4), 38 (Dec 2015)
9. Vardigan, M., Heus, P., Thomas, W.: Data Documentation Initiative: Toward a
Standard for the Social Sciences. International Journal of Digital Curation 3(1),
107–113 (Dec 2008)
10. Zapilko, B., Schaible, J., Mayr, P., Mathiak, B.: TheSoz: A SKOS Representation
of the Thesaurus for the Social Sciences p. 7 (2012)
11. Zapilko, B., Schaible, J., Wandhöfer, T., Mutschke, P.: Applying Linked Data
Technologies in the Social Sciences. KI - Künstliche Intelligenz 30(2), 159–162
(Jun 2016)

14
www.sora-projekt.de (note: German only)
15
https://www.diw.de/en/soep