The Ofness and Aboutness of Survey Data: Improved Indexing of Social Science Questionnaires

Friedrich, Tanja; Siegers, Pascal

doi:10.1007/978-3-319-25226-1_54

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Although it accounts for a smaller part of the information need aspects (9%), it is an important aspect of social science data. A similar concept has been described by Friedrich & Siegers (2016), who describe the "aboutness" of survey questions. In summary, this shows that concepts exist in specialized thesauri (e.g., the TheSoz) but are not entirely present in metadata standards (e.g., DDI).…”

Section: Discussionmentioning

confidence: 76%

Genuine Information Needs of Social Scientists Looking for Data

Papenmeier

Krämer

Friedrich

et al. 2021

Proceedings of the Association for Information Science and Tech

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Publishing research data is widely expected to increase its reuse and to inspire new research. In the social sciences, data from surveys, interviews, polls, and statistics are primary resources for research. There is a long tradition to collect and offer research data in data archives and online repositories. Researchers use these systems to identify data relevant to their research. However, especially in data search, users' complex information needs seem to collide with the capabilities of data search systems. The search capabilities, in turn, depend to a high degree upon the metadata schemes used to describe the data. In this research, we conducted an online survey with 72 social science researchers who expressed their individual information needs for research data like they would do when asking a colleague for help. We analyzed these information needs and attributed their different components to the categories: topic, metadata, and intention. We compared these categories and their content to existing metadata models of research data and the search and filter opportunities offered in existing data search systems. We found a mismatch between what users have as a requirement for their data and what is offered on metadata level and search system possibilities.

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Section: Discussionmentioning

confidence: 76%

Genuine Information Needs of Social Scientists Looking for Data

Papenmeier

Krämer

Friedrich

et al. 2021

Proceedings of the Association for Information Science and Tech

Self Cite

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“…Therefore, we focus on extracting and representing additional information from a question that go beyond tagging the questions with keywords and topics. Our approach is based on the ofness and aboutness concept of survey data introduced by [ 10 ]. While ofness refers to the literal question wording, which often reveals information about the topic of a question, the aboutness relates to the latent content.…”

Section: Introductionmentioning

confidence: 99%

Semantic Annotation, Representation and Linking of Survey Data

Bensmann

Papenmeier

Kern

et al. 2020

Semantic Systems. In the Era of Knowledge Graphs

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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.

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