Named Entity Recognition and Knowledge Linkage Jonathan Baum University of Applied Sciences, Darmstadt jonathan.baum (at) stud.h-da.de Abstract Named entity recognition (NER) is an impor- tant method for Natural Language Process- ing. In this work, the Named Entity Recogni- tion model FLERT XLM-R by Schweter and Akbik (2021) is investigated with respect to Bertuch (1790) Bilderbuch f¨ur Kinder (1790- 1830). The objective is to successfully ap- ply the model for recognizing named entities of the book Bilderbuch f¨ur Kinder and to link the recognized entities to corresponding Wiki- data items. Achieving this undertaking leads to linking the entities to the current knowledge of the Wikidata database and gives researchers the opportunity to gain further knowledge in the historical education field. To achieve this goal, the author of this paper developed a data curation pipeline, starting with the extraction of named entities, followed by the creation of a gold-standard validated with the Inter- Annotator Agreement (IAA). The result of the extracted entities has been approved using F- measure and was processed through OpenRe- fine. The delineated procedure can be used to create similar knowledge linkages.1 1 Introduction Bertuch’s Bilderbuch f¨ur Kinder is a 19th cen- tury encyclopedia for children. Friedrich Justin Bertuch published the first booklet of the afore- mentioned book in 1790. Five panels each are accompanied by short, easy-to-understand texts, which were intended to be ”appropriate to the powers of understanding of a child” (Bertuch, 1790). To keep the children’s attention, the book addresses exotic and unusual topics (Kollmann, 2021). Each panel is accompanied by a one-page description. Usually, a short paragraph introduces 1The source code of this project is available upon request: https://code.fbi.h-da.de/stjtbaum/ bachelor_thesis the respective panel topic and the individual fig- ures or objects in the pictures are given an individ- ual description. The Semanitc MediaWiki (SMW) platform Interlinking Pictura2 enables a seman- tic view of the data of Bertuch’s Bilderbuch f¨ur Kinder. The picture book for children by Bertuch is a closed, firmly defined collection with a vol- ume of about 6k individual figures, which are rep- resented on 1185 plates. In order to gain further knowledge in the histor- ical education field about the entities in Bertuch’s encyclopedia, the recognition of named entities is a necessary task. For example, changes in the spelling (over time) of entities can be stud- ied. Since the 18th century, certain names of plants, countries and animals have changed - for instance, Vandiemensland has been known as Tas- mania since the 19th century. The state of the art in Natural Language Pro- cessing has been constantly evolving and reaching new heights compared to recent years. With an F-measure of 92.23, using the German CoNLL- 20033 dataset, the NER model FLERT XLM- R (Schweter and Akbik, 2021) is at the leading edge of current NER systems. This state-of-the- art named entity recognition model uses machine learning methods and has become the focus of my investigation, due to its popularity, relevance, code documentation and outstanding results for the Ger- man NER. The model by Schweter and Akbik (2021) was published within flairNLP4 and uses document-level features, word embeddings, and transformer models. The pipeline proposed in the following chapter describes the complete data curation process. 2https://interlinking.bbf.dipf.de/ index.php/Hauptseite 3https://www.clips.uantwerpen.be/ conll2003/ner/ 4https://github.com/flairNLP/flair