Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences (Extended Version)

Lars Heling; Felix Bensmann; Benjamin Zapilko; Maribel Acosta; York Sure-Vetter

doi:10.1007/978-3-030-32327-1_48

Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences (Extended Version)

Lars Heling, Felix Bensmann, Benjamin Zapilko, Maribel Acosta, York Sure-Vetter

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Many research endeavors in the social sciences rely on high-quality empirical data. Survey data is often used as a foundation to investigate social 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 integration and analysis of relevant data is a time-consuming process for researchers. This is due to the fact that search, discovery, and retrieval of the survey data requires accessing 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 architecture is a 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.

Original language	English
Title of host publication	The Semantic Web
Subtitle of host publication	ESWC 2019 Satellite Events - ESWC 2019 Satellite Events, Revised Selected Papers
Editors	Pascal Hitzler, Sabrina Kirrane, Olaf Hartig, Victor de Boer, Stefan Schlobach, Maria-Esther Vidal, Maria Maleshkova, Karl Hammar, Nelia Lasierra, Steffen Stadtmüller, Katja Hose, Ruben Verborgh
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	285-299
Number of pages	15
ISBN (Print)	9783030323264
DOIs	https://doi.org/10.1007/978-3-030-32327-1_48
State	Published - 2019
Externally published	Yes
Event	16th Extended Semantic Web Conference, ESWC 2019 - Portoroz, Slovenia Duration: 2 Jun 2019 → 6 Jun 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11762 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th Extended Semantic Web Conference, ESWC 2019
Country/Territory	Slovenia
City	Portoroz
Period	2/06/19 → 6/06/19

Keywords

DDI
Knowledge Graph
RDF
Survey data

Access to Document

10.1007/978-3-030-32327-1_48

Cite this

Heling, L., Bensmann, F., Zapilko, B., Acosta, M., & Sure-Vetter, Y. (2019). Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences (Extended Version). In P. Hitzler, S. Kirrane, O. Hartig, V. de Boer, S. Schlobach, M.-E. Vidal, M. Maleshkova, K. Hammar, N. Lasierra, S. Stadtmüller, K. Hose, & R. Verborgh (Eds.), The Semantic Web: ESWC 2019 Satellite Events - ESWC 2019 Satellite Events, Revised Selected Papers (pp. 285-299). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11762 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32327-1_48

Heling, Lars ; Bensmann, Felix ; Zapilko, Benjamin et al. / Building Knowledge Graphs from Survey Data : A Use Case in the Social Sciences (Extended Version). The Semantic Web: ESWC 2019 Satellite Events - ESWC 2019 Satellite Events, Revised Selected Papers. editor / Pascal Hitzler ; Sabrina Kirrane ; Olaf Hartig ; Victor de Boer ; Stefan Schlobach ; Maria-Esther Vidal ; Maria Maleshkova ; Karl Hammar ; Nelia Lasierra ; Steffen Stadtmüller ; Katja Hose ; Ruben Verborgh. Springer Science and Business Media Deutschland GmbH, 2019. pp. 285-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{366466384aef40aaa175557d8f228500,

title = "Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences (Extended Version)",

abstract = "Many research endeavors in the social sciences rely on high-quality empirical data. Survey data is often used as a foundation to investigate social 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 integration and analysis of relevant data is a time-consuming process for researchers. This is due to the fact that search, discovery, and retrieval of the survey data requires accessing 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 architecture is a 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 = "DDI, Knowledge Graph, RDF, Survey data",

author = "Lars Heling and Felix Bensmann and Benjamin Zapilko and Maribel Acosta and York Sure-Vetter",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 16th Extended Semantic Web Conference, ESWC 2019 ; Conference date: 02-06-2019 Through 06-06-2019",

year = "2019",

doi = "10.1007/978-3-030-32327-1_48",

language = "English",

isbn = "9783030323264",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "285--299",

editor = "Pascal Hitzler and Sabrina Kirrane and Olaf Hartig and {de Boer}, Victor and Stefan Schlobach and Maria-Esther Vidal and Maria Maleshkova and Karl Hammar and Nelia Lasierra and Steffen Stadtm{\"u}ller and Katja Hose and Ruben Verborgh",

booktitle = "The Semantic Web",

}

Heling, L, Bensmann, F, Zapilko, B, Acosta, M & Sure-Vetter, Y 2019, Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences (Extended Version). in P Hitzler, S Kirrane, O Hartig, V de Boer, S Schlobach, M-E Vidal, M Maleshkova, K Hammar, N Lasierra, S Stadtmüller, K Hose & R Verborgh (eds), The Semantic Web: ESWC 2019 Satellite Events - ESWC 2019 Satellite Events, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11762 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 285-299, 16th Extended Semantic Web Conference, ESWC 2019, Portoroz, Slovenia, 2/06/19. https://doi.org/10.1007/978-3-030-32327-1_48

Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences (Extended Version). / Heling, Lars; Bensmann, Felix; Zapilko, Benjamin et al.
The Semantic Web: ESWC 2019 Satellite Events - ESWC 2019 Satellite Events, Revised Selected Papers. ed. / Pascal Hitzler; Sabrina Kirrane; Olaf Hartig; Victor de Boer; Stefan Schlobach; Maria-Esther Vidal; Maria Maleshkova; Karl Hammar; Nelia Lasierra; Steffen Stadtmüller; Katja Hose; Ruben Verborgh. Springer Science and Business Media Deutschland GmbH, 2019. p. 285-299 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11762 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Building Knowledge Graphs from Survey Data

T2 - 16th Extended Semantic Web Conference, ESWC 2019

AU - Heling, Lars

AU - Bensmann, Felix

AU - Zapilko, Benjamin

AU - Acosta, Maribel

AU - Sure-Vetter, York

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - Many research endeavors in the social sciences rely on high-quality empirical data. Survey data is often used as a foundation to investigate social 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 integration and analysis of relevant data is a time-consuming process for researchers. This is due to the fact that search, discovery, and retrieval of the survey data requires accessing 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 architecture is a 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.

AB - Many research endeavors in the social sciences rely on high-quality empirical data. Survey data is often used as a foundation to investigate social 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 integration and analysis of relevant data is a time-consuming process for researchers. This is due to the fact that search, discovery, and retrieval of the survey data requires accessing 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 architecture is a 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.

KW - DDI

KW - Knowledge Graph

KW - RDF

KW - Survey data

UR - http://www.scopus.com/inward/record.url?scp=85075579303&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-32327-1_48

DO - 10.1007/978-3-030-32327-1_48

M3 - Conference contribution

AN - SCOPUS:85075579303

SN - 9783030323264

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 285

EP - 299

BT - The Semantic Web

A2 - Hitzler, Pascal

A2 - Kirrane, Sabrina

A2 - Hartig, Olaf

A2 - de Boer, Victor

A2 - Schlobach, Stefan

A2 - Vidal, Maria-Esther

A2 - Maleshkova, Maria

A2 - Hammar, Karl

A2 - Lasierra, Nelia

A2 - Stadtmüller, Steffen

A2 - Hose, Katja

A2 - Verborgh, Ruben

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 2 June 2019 through 6 June 2019

ER -

Heling L, Bensmann F, Zapilko B, Acosta M, Sure-Vetter Y. Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences (Extended Version). In Hitzler P, Kirrane S, Hartig O, de Boer V, Schlobach S, Vidal ME, Maleshkova M, Hammar K, Lasierra N, Stadtmüller S, Hose K, Verborgh R, editors, The Semantic Web: ESWC 2019 Satellite Events - ESWC 2019 Satellite Events, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2019. p. 285-299. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32327-1_48

Building Knowledge Graphs from Survey Data: A Use Case in the Social Sciences (Extended Version)

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this