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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.
The scientific innovation process embraces the steps from problem definition through the development and evaluation of innovative solutions to their successful exploitation. The challenges imposed by this process can be answered by the creation of a powerful and flexible next-generation e-Science infrastructure, which exploits leading edge information and knowledge technologies and enables a comprehensive and intelligent means of supporting this process. This paper describes our vision of a Knowledge-based eScience infrastructure, which is based on the results of an in-depth study of the researchers requirements. Furthermore, it introduces the Fraunhofer e-Science Cockpit as a first implementation of our vision.
The correspondence between the terminology used for querying and the one used in content objects to be retrieved, is a crucial prerequisite for effective retrieval technology. However, as terminology is evolving over time, a growing gap opens up between older documents in (long-term) archives and the active language used for querying such archives. Thus, technologies for detecting and systematically handling terminology evolution are required to ensure "semantic" accessibility of (Web) archive content on the long run. As a starting point for dealing with terminology evolution this paper formalizes the problem and discusses issues, first ideas and relevant technologies.
Web archives created by the Internet Archive (IA) (https://archive.org), national libraries and other archiving services contain large amounts of information collected for a time period of over twenty years. These archives constitute a valuable source for research in many disciplines, including the digital humanities and the historical sciences by offering a unique possibility to look into past events and their representation on the Web.
Most Web archive services aim to capture the entire Web (IA) or national top-level domains and are therefore broad in their scope, diverse regarding the topics they contain and the time intervals they cover. Due to the large size and the broad scope it is difficult for interested researchers to locate relevant information in the archives as search facilities are very limited. Many users are more interested in studying smaller and topically coherent event-centric collections of documents contained in a Web archive [1,2]. Such collections can reflect specific events such as elections, or natural disasters, e.g. the Fukushima nuclear disaster (2011) or the German federal elections.
The Specialised Information Service Performing Arts (SIS PA) is part of a funding programme by the German Research Foundation that enables libraries to develop tailor-made services for individual disciplines in order to provide researchers direct access to relevant materials and resources from their field. For the field of performing arts, the SIS PA is aggregating metadata about theater and dance resources from currently, mostly, German-speaking cultural heritage institutions in a VuFind-based search portal.
In this article, we focus on metadata quality and its impact on the aggregation workflow by describing the different, possibly data provider-specific, process stages of improving data quality in order to achieve a searchable, interlinked knowledge base. We also describe lessons learned and limitations of the process.
Biodiversity research heavily relies on recent and older literature, and the data contained therein. Despite great effort, large parts of the literature and the data it holds are still not available in appropriate formats needed for efficient compilation and analysis. As a part of the current funding strategy of the German Research Council (Deutsche Forschungsgemeinschaft, DFG), and resulting from an extensive dialogue with the scientific community in Germany, a "Specialised Information Service" (Fachinformationsdienst, FID) for Biodiversity Research will be established with the objective of making further segments of literature about biodiversity available in up-to-date formats. This project, starting 2017, is conducted by the University Library Johann Christian Senckenberg (Frankfurt/Main, Germany) together with the Senckenberg Gesellschaft für Naturforschung and the Text Technology Lab of the Goethe University (Frankfurt/Main).
The new Specialised Information Service for Biodiversity Research (FID Biodiversitätsforschung) comprises four core elements: (A) A text mining approach which encompasses advanced text technologies and a large body of 20th century literature; (B) the digitisation of selected German biodiversity literature; (C) a platform für Open Access journals; and (D) Acquisition of specialised print literature.
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 professional e-journals will be established. All generated data will also be made accessible through GFBio (German Federation for Biological Data). BIOfid is supported by the LIS-Scientific Library Services and Information Systems program of the German Research Foundation (DFG).
This paper introduces a novel research tool for the field of linguistics: The Linjgujisjtik web portal provides a virtual library which offers scientific information on every linguistic subject. It comprises selected internet sources and databases as well as catalogues for linguistic literature, and addresses an interdisciplinary audience. The virtual library is the most recent outcome of the Special Subject Collection Linguistics of the German Research Foundation (DFG), and also integrates the knowledge accumulated in the Bibliography of Linguistic Literature. In addition to the portal, we describe long-term goals and prospects with a special focus on ongoing efforts regarding an extension towards integrating language resources and Linguistic Linked Open Data.