Footnote 1 However, information on species characteristics and occurrences does not necessarily follow a standard vocabulary, nor can it usually be found coherently in narrative texts. Which species occurred when and where? This is a basic question to understand the biogeography of a species, to define its ecological preferences, and to estimate its adaptability and abundance in certain habitats. Additionally, due to numerous taxonomic revisions, a large number of synonyms and homonyms have emerged in the course of history, further affecting the (automatic) assignment of taxonomic names from texts to biological taxa. sylvatica” instead of “ Fagus sylvatica”) or even using their full length version including the authority and the year of publication (“ Fagus sylvatica L., 1753”). There is also the practice of abbreviating scientific names (e.g., “ F. That naming follows an internationally accepted taxonomic nomenclature but names can also be given in vernacular forms, which often varies both regionally and temporally. A central problem is the naming of biological organisms (Akella et al., 2012 Koning et al., 2005). With regard to life sciences articles, there are also some special features that should be taken into account in semantic text analysis. But even if natural language texts are digitized, efficient information extraction (extracting mentions of entities and the relations between them) may not be available, since current natural language processing tools in biodiversity science have limited application range and still require testing (Thessen et al., 2012). Legacy scientific literature, for example, usually falls into the latter category, because historical writings are often only available in printed form. Even though scientists adopt these principles gradually as best practices, many studies will remain “below radar level” for various reasons. FAIR data principles (Wilkinson et al., 2016) should therefore ensure a sustainable research data management to make data findable, accessible, interoperable and reusable. ![]() Data on species occurrences and their adaptations to changing environmental conditions serve as an important basis for studying their distribution patterns and potential threats. Finally, some general lessons, in particular with multiple annotation projects, are drawn.Īnthropocene biodiversity loss has been one of the core issues in earth and life sciences for years (Cardoso et al., 2020 Hallmann et al., 2017 Johnson et al., 2017 Seddon et al., 2016). The tools used to create the annotations are introduced, and the use of the data in the semantic portal is described. We describe the design decisions and the genuine Annotation Hub Framework underlying the bio fid annotations and present agreement results. A special feature of bio fid 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. ![]() To this end, among others, we gathered the bio fid text corpus, which is a cooperatively built resource, developed by biologists, text technologists, and linguists. Such methods draw on machine learning techniques, which in turn are trained by learning data. In particular, text-technological information extraction methods were needed, which extract the required information from the texts. However, to make such a portal work, a couple of methods had to be developed or adapted first. ![]() To improve the access to semantic biodiversity information, we have launched the BIOfid project ( and have developed a portal to access the semantics of German language biodiversity texts, mainly from the 19th and 20th century. ![]() 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. Biodiversity information is contained in countless digitized and unprocessed scholarly texts.
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