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.

BIOfid is a specialized information service currently being developed to mobilize biodiversity data dormant in printed historical and modern literature and to offer a platform for open access journals on the science of biodiversity. Our team of librarians, computer scientists and biologists produce high-quality text digitizations, develop new text-mining tools and generate detailed ontologies enabling semantic text analysis and semantic search by means of user-specific queries. In a pilot project we focus on German publications on the distribution and ecology of vascular plants, birds, moths and butterflies extending back to the Linnaeus period about 250 years ago.

In order to promote the accessibility of biodiversity data in historic and contemporary literature, we introduce a new interdisciplinary project called BIOfid (FID= Fachinformationsdienst, a service for providing specialized information). The project aims at a mobilization of data available in print only by combining digitization of scientific biodiversity literature with the development of innovative text mining tools for complex, eventually semantic searches throughout the complete text corpus. A major prerequisite for the development of such search tools is the provision of sophisticated anatomy ontologies on the one hand, and of complete lists of species names (currently considered valid as well as all synonyms) at a global scale on the other hand. In the initial stage, we chose examples from German publications of the past 250 years dealing with the geographic distribution and ecology of vascular plants (Tracheophyta), birds (Aves), as well as moths and butterflies (Lepidoptera) in Germany. These taxa have been prioritized according to current demands of German research groups (about 50 sites) aiming at analyses and modeling of distribution patterns and their changes through time. In the long term, we aim at providing data and open source software applicable for any taxon and geographic region. For this purpose, a platform for open access journals for long-term availability of ‡, § | ‡ ¶ ‡ |

Schriftenschau

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.