Barbara Piringer’s research while affiliated with Austrian Academy of Sciences (OeAW) and other places


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Publications (15)


Crafting a System for Knowledge Discovery and Organisation: A Case-Study on KOS for a Non-Standard German Legacy Dataset
  • Chapter

January 2020

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14 Reads

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Enric Senabre

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Barbara Piringer

The proceedings explore knowledge organization systems and their role in knowledge organization, knowledge sharing, and information searching. The papers cover a wide range of topics related to knowledge transfer, representation, concepts and conceptualization, social tagging, domain analysis, music classification, fiction genres, museum organization. The papers discuss theoretical issues related to knowledge organization and the design, development and implementation of knowledge organizing systems as well as practical considerations and solutions in the application of knowledge organization theory. Covered is a range of knowledge organization systems from classification systems, thesauri, metadata schemas to ontologies and taxonomies.



Uncertain spaces, uncertain places - dealing with geographic information in Digital Humanities on the example of a language legacy data set
  • Presentation
  • File available

July 2019

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85 Reads

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Figure 1: Paper slips and Questionnaires of the "Hauptkatalog" / analogous data collection
Figure 2+3: Spatio-temporal approach for querying and displaying data in the map: map 1 (blue): query for the word "Karotte" (carrot) in 1966. map 2 (red, orange): query for the word "Karotte" in 2001. © Emanuel Hrastnig et al. @ exploreAT!
Figure 5: Concept lights V.1.0: Visual approaches to discover semantic concepts in a collection based on questionnaires. © Alejandro Benito et al. @ exploreAT!
The unknown and the uncertain. A data discovery journey from an analogous data collection to an interactive exploration space

December 2018

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106 Reads

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1 Citation


Semantic Modelling and Publishing of Traditional Data Collection Questionnaires and Answers

November 2018

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364 Reads

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16 Citations

Information

Extensive collections of data of linguistic, historical and socio-cultural importance are stored in libraries, museums and national archives with enormous potential to support research. However, a sizable portion of the data remains underutilised because of a lack of the required knowledge to model the data semantically and convert it into a format suitable for the semantic web. Although many institutions have produced digital versions of their collection, semantic enrichment, interlinking and exploration are still missing from digitised versions. In this paper, we present a model that provides structure and semantics to a non-standard linguistic and historical data collection on the example of the Bavarian dialects in Austria at the Austrian Academy of Sciences. We followed a semantic modelling approach that utilises the knowledge of domain experts and the corresponding schema produced during the data collection process. The model is used to enrich, interlink and publish the collection semantically. The dataset includes questionnaires and answers as well as supplementary information about the circumstances of the data collection (person, location, time, etc.). The semantic uplift is demonstrated by converting a subset of the collection to a Linked Open Data (LOD) format, where domain experts evaluated the model and the resulting dataset for its support of user queries.



Figure 3. Threefold approach to addressing uncertainties in Topotheque (AT). © Eveline Wandl-Vogt, Thomas Palfinger, Barbara Piringer 2018
Figure 4. Example of an image analyzed with the three computer vision tools. Picture source: Topothek Wieselburg. http://wieselburg.topothek.at/#doc=311992. Copyright notice: Urheber: Stadtarchiv, Foto-Dufek-1437/25A
Applying Commercial Computer Vision Tools to Cope with Uncertainties in a Citizen-driven Archive: The case study Topothek@exploreAT!

October 2018

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54 Reads

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1 Citation

Uncertainties in data, e.g., incomplete data sets, data quality issues or inconsistencies in annotations, are a common phenomenon across disciplines. How to address these issues is context dependent. In this paper, we address uncertainties in the citizen-driven archive Topotheque as a concrete use-case in the Digital Humanities project exploreAT!, and demonstrate, how to deal with uncertainties by benchmarking a set of selected commercial computer vision (CV) tools. The approach aims to enrich Topotheque's data to enable better access, connectivity and analysis for both researchers and citizens. Results show that by applying CV, existing uncertainties are noticeably reduced, but new ones also introduced. Better grounds for semantic structuring are provided, enabling higher connectivity and linking within Topotheque, but also across other data sets. Ultimately, the enrichment of the archive is for the benefit of both researchers and citizens enabled by addressing and tackling apparent uncertainties.


A Semantic Model for Traditional Data Collection Questionnaires Enabling Cultural Analysis

May 2018

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103 Reads

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10 Citations

Around the world, there is a wide range of traditional data manually collected for different scientific purposes. A small portion of this data has been digitised, but much of it remains less usable due to a lack of rich semantic models to enable humans and machines to understand, interpret and use these data. This paper presents ongoing work to build a semantic model to enrich and publish traditional data collection questionnaires in particular, and the historical data collection of the Bavarian Dialects in Austria in general. The use of cultural and linguistic concepts identified in the questionnaire questions allow for cultural exploration of the non-standard data (answers) of the collection. The approach focuses on capturing the semantics of the questionnaires dataset using domain analysis and schema analysis. This involves analysing the overall data collection process (domain analysis) and analysing the various schema used at different stages (schema analysis). By starting with modelling the data collection method, the focus is placed on the questionnaires as a gateway to understanding, interlinking and publishing the datasets. A model that describes the semantic structure of the main entities such as questionnaires, questions, answers and their relationships is presented.


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Citations (3)


... Above all the challenges, early-stage data and informal feedback from the students indicate that the MaaS design is working well with promising results. Future work in the area will look into the standardisation and dissemination of the MaaS approach using semantic models (Abgaz et al., 2018, Phal et al., 2010 to make it available for the wider academic community. ...

Reference:

Designing Microcurricula-as-a-service: The case of large class, cross programme, and online asynchronous module
Semantic Modelling and Publishing of Traditional Data Collection Questionnaires and Answers

Information

... In relation to Digital Humanities (DH), researchers have been exhorted to embrace data driven approaches to do science, inundated by sheer amounts of data, both from legacy and from modern systems and sources [14]. In this sense, the question of certainty around data becomes crucial, and some DH scholars have started to identify how to address uncertainty with data visualizations [15], as well as the importance to determine taxonomies around uncertainty. [16], based on [17], have proposed a classification for epistemic uncertainty (which is the type of uncertainty that can be reducible by new information) differentiating between (1) Imprecision (differing values within a contested category), (2) Ignorance (lack of context), (3) Credibility (error or bias seen to be entering the system) and (4) Incompleteness (outlier record where information is missing). ...

Applying Commercial Computer Vision Tools to Cope with Uncertainties in a Citizen-driven Archive: The case study Topothek@exploreAT!

... One of the globally first examples of such a database, linked with a dictionary content is the "Datenbank der bairischen Mundarten in Österreich electronically mapped (dbo@ema)" Wandl-Vogt 2008). However, it is difficult to combine and query different linguistic, especially dialectal, datasets in an integrated manner, since most datasets lack semantic interoperability (Scholz et al., 2017;Abgaz et al., 2018). ...

A Semantic Model for Traditional Data Collection Questionnaires Enabling Cultural Analysis