The article presents the results of a survey on dictionary use in Europe, focusing on general monolingual dictionaries. The survey is the broadest survey of dictionary use to date, covering close to 10,000 dictionary users (and non-users) in nearly thirty countries. Our survey covers varied user groups, going beyond the students and translators who have tended to dominate such studies thus far. The survey was delivered via an online survey platform, in language versions specific to each target country. It was completed by 9,562 respondents, over 300 respondents per country on average. The survey consisted of the general section, which was translated and presented to all participants, as well as country-specific sections for a subset of 11 countries, which were drafted by collaborators at the national level. The present report covers the general section. IntroductionResearch into dictionary use has become increasingly important in recent years. In contrast to 15 years ago, new findings in this area are presented every year, e.g. at every Euralex or eLex conference. These studies range from questionnaire or log file studies to smaller-scale studies focussing on eye tracking, usability, or other aspects of dictionary use measurable in a lab. For an overview of different studies,
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.
Cultural heritage images are among the primary media for communicating and preserving the cultural values of a society. The images represent concrete and abstract content and symbolise the social, economic, political, and cultural values of the society. However, an enormous amount of such values embedded in the images is left unexploited partly due to the absence of methodological and technical solutions to capture, represent, and exploit the latent information. With the emergence of new technologies and availability of cultural heritage images in digital formats, the methodology followed to semantically enrich and utilise such resources become a vital factor in supporting users need. This paper presents a methodology proposed to unearth the cultural information communicated via cultural digital images by applying Artificial Intelligence (AI) technologies (such as Computer Vision (CV) and semantic web technologies). To this end, the paper presents a methodology that enables efficient analysis and enrichment of a large collection of cultural images covering all the major phases and tasks. The proposed method is applied and tested using a case study on cultural image collections from the Europeana platform. The paper further presents the analysis of the case study, the challenges, the lessons learned, and promising future research areas on the topic.
Different types of uncertainties occur in almost all datasets and are an inherent property of data across different academic disciplines, including digital humanities (DH). In this paper, we address, demonstrate and analyse spatio-temporal uncertainties in a non-standard German legacy dataset in a DH context. Although the data collection is primarily a linguistic resource, it contains a wealth of additional, comprehensive information, such as location and temporal detail. The addressed uncertainties have manifested because of a variety of reasons, and partly also because of decades of data transformation processes. We here propose our own taxonomy for capturing and classifying the various uncertainties, and show with numerous examples how the remedying but also re-introduction of uncertainties affects DH practices.Informatics 2019, 6, 34 2 of 29 with from the point of view of risk assessment, measurements of uncertainty or methods of removing uncertainty to attain higher degrees of certainty (cf. [12]).In this context, a distinction can be made in how uncertainty is dealt with, for example, in the fields of natural sciences in contrast to the humanities. Whilst uncertainties in natural sciences are mostly related to the expected limits in the possibilities of making measurements and also inherent to the statistical properties of what can be inferred on the empirical samples, uncertainties in the humanities, however, can also involve subjective aspects related to perception, ambiguity, vagueness, incompleteness, credibility, etc.The research purpose of the present paper thus evolves out of the uniqueness of the legacy collection dealt with here. The types of uncertainties encountered in humanities datasets already differ from those more typical in the natural sciences. Our specific non-standard dataset, and the focus on spatio-temporal aspects, makes it a necessity to devise our own taxonomy in order to fully capture the relevant uncertainties.The paper is thus structured as follows: Section 1 (Introduction) presents an overview of existing and relevant taxonomies dealing with one or more of the aspects (temporal, spatial, uncertainties) central to the research purpose, and also an outline of the diverse contexts that uncertainty has been dealt with in the digital humanities field. In Section 2 (Materials and Methods), the specific dataset is illustrated, followed by a pointer to previous analyses of spatial and temporal aspects. In addition, the methods of devising our taxonomy are outlined. Section 3 (Results) presents the DBÖ (Datenbank der bairischen Mundarten in Österreich/Database of Bavarian Dialects in Austria) taxonomy of uncertainties illustrated with detailed examples of its specific categories. Finally, Section 4 (Discussion/Conclusion) discusses the newly composed taxonomy against the background of existing taxonomies that have been reviewed in Section 1. Aside from this, reflections on how the remedying or re-introduction of uncertainties affects digital humanities (DH) practice are provided.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.