This paper provides a theoretical assessment of gestures in the context of authoring image-related hypertexts by example of the museum information system WikiNect. To this end, a first implementation of gestural writing based on image schemata is provided (Lakoff in Women, fire, and dangerous things: what categories reveal about the mind. University of Chicago Press, Chicago, 1987). Gestural writing is defined as a sort of coding in which propositions are only expressed by means of gestures. In this respect, it is shown that image schemata allow for bridging between natural language predicates and gestural manifestations. Further, it is demonstrated that gestural writing primarily focuses on the perceptual level of image descriptions (Hollink et al. in Int J Hum Comput Stud 61(5): 2004). By exploring the metaphorical potential of image schemata, it is finally illustrated how to extend the expressiveness of gestural writing in order to reach the conceptual level of image descriptions. In this context, the paper paves the way for implementing museum information systems like WikiNect as systems of kinetic hypertext authoring based on full-fledged gestural writing.
Biodiversity information is contained in countless digitized and unprocessed scholarly texts. Although automated extraction of these data has been gaining momentum for years, there are still innumerable text sources that are poorly accessible and require a more advanced range of methods to extract relevant information. To improve the access to semantic biodiversity information, we have launched the BIOfid project (www.biofid.de) and have developed a portal to access the semantics of German language biodiversity texts, mainly from the 19th and 20th century. However, to make such a portal work, a couple of methods had to be developed or adapted first. In particular, text-technological information extraction methods were needed, which extract the required information from the texts. Such methods draw on machine learning techniques, which in turn are trained by learning data. To this end, among others, we gathered the bio text corpus, which is a cooperatively built resource, developed by biologists, text technologists, and linguists. A special feature of bio is its multiple annotation approach, which takes into account both general and biology-specific classifications, and by this means goes beyond previous, typically taxon- or ontology-driven proper name detection. We describe the design decisions and the genuine Annotation Hub Framework underlying the bio annotations and present agreement results. The tools used to create the annotations are introduced, and the use of the data in the semantic portal is described. Finally, some general lessons, in particular with multiple annotation projects, are drawn.
The storage of data in public repositories such as the Global Biodiversity Information Facility (GBIF) or the National Center for Biotechnology Information (NCBI) is nowadays stipulated in the policies of many publishers in order to facilitate data replication or proliferation. Species occurrence records contained in legacy printed literature are no exception to this. The extent of their digital and machine-readable availability, however, is still far from matching the existing data volume (Thessen and Parr 2014). But precisely these data are becoming more and more relevant to the investigation of ongoing loss of biodiversity. In order to extract species occurrence records at a larger scale from available publications, one has to apply specialised text mining tools. However, such tools are in short supply especially for scientific literature in the German language.
The Specialised Information Service Biodiversity Research*1 BIOfid (Koch et al. 2017) aims at reducing this desideratum, inter alia, by preparing a searchable text corpus semantically enriched by a new kind of multi-label annotation. For this purpose, we feed manual annotations into automatic, machine-learning annotators. This mixture of automatic and manual methods is needed, because BIOfid approaches a new application area with respect to language (mainly German of the 19th century), text type (biological reports), and linguistic focus (technical and everyday language).
We will present current results of the performance of BIOfid’s semantic search engine and the application of independent natural language processing (NLP) tools. Most of these are freely available online, such as TextImager (Hemati et al. 2016). We will show how TextImager is tied into the BIOfid pipeline and how it is made scalable (e.g. extendible by further modules) and usable on different systems (docker containers).
Further, we will provide a short introduction to generating machine-learning training data using TextAnnotator (Abrami et al. 2019) for multi-label annotation. Annotation reproducibility can be assessed by the implementation of inter-annotator agreement methods (Abrami et al. 2020). Beyond taxon recognition and entity linking, we place particular emphasis on location and time information. For this purpose, our annotation tag-set combines general categories and biology-specific categories (including taxonomic names) with location and time ontologies. The application of the annotation categories is regimented by annotation guidelines (Lücking et al. 2020). Within the next years, our work deliverable will be a semantically accessible and data-extractable text corpus of around two million pages. In this way, BIOfid is creating a new valuable resource that expands our knowledge of biodiversity and its determinants.
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