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The accessibility of the increasingly digitally available historical collections of the Berlin State Library (SBB) can be expanded in the future based on textual contents. Within the Data Science department appropriate solutions for the recognition and linking of Named Entities and thus automatically generated semantic enrichments have been developed. The article describes how entities are being identified, classified and linked with the help of technologies from the field of artificial intelligence and machine learning, employing Wikidata and Wikipedia as knowledge bases. We present a case study on Topic Modeling with Named Entities and discuss future perspectives for further development. [ABSTRACT FROM AUTHOR]