Details
| Original language | English |
|---|---|
| Title of host publication | Semantic Systems. In the Era of Knowledge Graphs |
| Subtitle of host publication | 16th International Conference on Semantic Systems, SEMANTiCS 2020, Proceedings |
| Editors | Eva Blomqvist, Paul Groth, Victor de Boer, Tassilo Pellegrini, Mehwish Alam, Tobias Käfer, Peter Kieseberg, Sabrina Kirrane, Albert Meroño-Peñuela, Harshvardhan J. Pandit |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 53-69 |
| Number of pages | 17 |
| ISBN (electronic) | 978-3-030-59833-4 |
| ISBN (print) | 9783030598327 |
| Publication status | Published - 2020 |
| Event | 16th International Conference on Semantic Systems, SEMANTiCS 2020 - Amsterdam, Netherlands Duration: 7 Sept 2020 → 10 Sept 2020 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 12378 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Semantic technologies offer significant potential for improving data search applications. Ongoing work thrives to equip data catalogs with new semantic search features to supplement existing keyword search and browsing capabilities. In particular within the social sciences, searching and reusing data is essential to foster efficient research. In this paper, we introduce an approach and experimental results aimed at improving interoperability and findability of social sciences survey items. Our contributions include a conceptual model for semantically representing survey items and questions, detailing meaningful dimensions of items, as well as experimental results geared towards the automated prediction of such item features using state-of-the-art machine learning models. Dimensions of interest include, for instance, references to geolocation and time periods or the scope and style of particular questions. We define classification tasks using neural and traditional machine learning models combined with sentence structure features. Applications of our work include semantic and faceted search for questions as part of our GESIS Search. We also provide the lifted data as a knowledge graph via a SPARQL endpoint for further reuse and sharing.
Keywords
- Natural language processing, Question feature extraction, Semantic data modelling, Social sciences survey data
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Semantic Systems. In the Era of Knowledge Graphs: 16th International Conference on Semantic Systems, SEMANTiCS 2020, Proceedings. ed. / Eva Blomqvist; Paul Groth; Victor de Boer; Tassilo Pellegrini; Mehwish Alam; Tobias Käfer; Peter Kieseberg; Sabrina Kirrane; Albert Meroño-Peñuela; Harshvardhan J. Pandit. Springer Science and Business Media Deutschland GmbH, 2020. p. 53-69 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12378 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Semantic Annotation, Representation and Linking of Survey Data
AU - Bensmann, Felix
AU - Papenmeier, Andrea
AU - Kern, Dagmar
AU - Zapilko, Benjamin
AU - Dietze, Stefan
N1 - Funding Information: Funding. This work was partly funded by the DFG, grant no. 388815326; the VACOS project at GESIS.
PY - 2020
Y1 - 2020
N2 - Semantic technologies offer significant potential for improving data search applications. Ongoing work thrives to equip data catalogs with new semantic search features to supplement existing keyword search and browsing capabilities. In particular within the social sciences, searching and reusing data is essential to foster efficient research. In this paper, we introduce an approach and experimental results aimed at improving interoperability and findability of social sciences survey items. Our contributions include a conceptual model for semantically representing survey items and questions, detailing meaningful dimensions of items, as well as experimental results geared towards the automated prediction of such item features using state-of-the-art machine learning models. Dimensions of interest include, for instance, references to geolocation and time periods or the scope and style of particular questions. We define classification tasks using neural and traditional machine learning models combined with sentence structure features. Applications of our work include semantic and faceted search for questions as part of our GESIS Search. We also provide the lifted data as a knowledge graph via a SPARQL endpoint for further reuse and sharing.
AB - Semantic technologies offer significant potential for improving data search applications. Ongoing work thrives to equip data catalogs with new semantic search features to supplement existing keyword search and browsing capabilities. In particular within the social sciences, searching and reusing data is essential to foster efficient research. In this paper, we introduce an approach and experimental results aimed at improving interoperability and findability of social sciences survey items. Our contributions include a conceptual model for semantically representing survey items and questions, detailing meaningful dimensions of items, as well as experimental results geared towards the automated prediction of such item features using state-of-the-art machine learning models. Dimensions of interest include, for instance, references to geolocation and time periods or the scope and style of particular questions. We define classification tasks using neural and traditional machine learning models combined with sentence structure features. Applications of our work include semantic and faceted search for questions as part of our GESIS Search. We also provide the lifted data as a knowledge graph via a SPARQL endpoint for further reuse and sharing.
KW - Natural language processing
KW - Question feature extraction
KW - Semantic data modelling
KW - Social sciences survey data
UR - http://www.scopus.com/inward/record.url?scp=85096607361&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59833-4_4
DO - 10.1007/978-3-030-59833-4_4
M3 - Conference contribution
AN - SCOPUS:85096607361
SN - 9783030598327
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 53
EP - 69
BT - Semantic Systems. In the Era of Knowledge Graphs
A2 - Blomqvist, Eva
A2 - Groth, Paul
A2 - de Boer, Victor
A2 - Pellegrini, Tassilo
A2 - Alam, Mehwish
A2 - Käfer, Tobias
A2 - Kieseberg, Peter
A2 - Kirrane, Sabrina
A2 - Meroño-Peñuela, Albert
A2 - Pandit, Harshvardhan J.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Semantic Systems, SEMANTiCS 2020
Y2 - 7 September 2020 through 10 September 2020
ER -