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With the ongoing loss of global biodiversity, long-term recordings of species distribution patterns are increasingly becoming important to investigate the causes and consequences for their change. Therefore, the digitization of scientific literature, both modern and historical, has been attracting growing attention in recent years. To meet this growing demand the Specialised Information Service for Biodiversity Research (BIOfid) was launched in 2017 with the aim of increasing the availability and accessibility of biodiversity information. Closely tied to the research community the interdisciplinary BIOfid team is digitizing data sources of biodiversity related research and provides a modern and professional infrastructure for hosting and sharing them. As a pilot project, German publications on the distribution and ecology of vascular plants, birds, moths and butterflies covering the past 250 years are prioritized. Large parts of the text corpus defined in accordance with the needs of the relevant German research community have already been transferred to a machine-readable format and will be publicly accessible soon. Software tools for text mining, semantic annotation and analysis with respect to the current trends in machine learning are developed to maximize bioscientific data output through user-specific queries that can be created via the BIOfid web portal (https://www.biofid.de/). To boost knowledge discovery, specific ontologies focusing on morphological traits and taxonomy are being prepared and will continuously be extended to keep up with an ever-expanding volume of literature sources.
The Specialized Information Service Biodiversity Research (BIOfid) has been launched to mobilize valuable biological data from printed literature hidden in German libraries for over the past 250 years. In this project, we annotate German texts converted by OCR from historical scientific literature on the biodiversity of plants, birds, moths and butterflies. Our work enables the automatic extraction of biological information previously buried in the mass of papers and volumes. For this purpose, we generated training data for the tasks of Named Entity Recognition (NER) and Taxa Recognition (TR) in biological documents. We use this data to train a number of leading machine learning tools and create a gold standard for TR in biodiversity literature. More specifically, we perform a practical analysis of our newly generated BIOfid dataset through various downstream-task evaluations and establish a new state of the art for TR with 80.23% F-score. In this sense, our paper lays the foundations for future work in the field of information extraction in biology texts.
In an ideal world, extraction of machine-readable data and knowledge from natural-language biodiversity literature would be done automatically, but not so currently. The BIOfid project has developed some tools that can help with important parts of this highly demanding task, while certain parts of the workflow cannot be automated yet. BIOfid focuses on the 20th century legacy literature, a large part of which is only available in printed form. In this workshop, we will present the current state of the art in mobilisation of data from our corpus, as well as some challenges ahead of us. Together with the participants, we will exercise or explain the following tasks (some of which can be performed by the participants themselves, while other tasks currently require execution by our specialists with special equipment): Preparation of text files as an input; pre-processing with TextImager/TextAnnotator; semiautomated annotation and linking of named entities; generation of output in various formats; evaluation of the output. The workshop will also provide an outlook for further developments regarding extraction of statements from natural-language literature, with the long-term aim to produce machine-readable data from literature that can extend biodiversity databases and knowledge graphs.