The majority of transmitted information consists of written text, either printed or electronically. Extraction of this information from digital resources requires the identification of important entities. While Named Entity Recognition (NER) is an important task for the extraction of factual information and the construction of knowledge graphs, other information such as terminological concepts and relations between entities are of similar importance in the context of knowledge engineering, knowledge base enhancement and semantic search. While the majority of approaches focusses on NER recognition in the context of the World-Wide-Web and thus needs to cover the broad range of common knowledge, we focus in the present work on the recognition of entities in highly specialized domains and describe our approach to ontology-based entity recognition and annotation (OER). Our approach, implemented as a first prototype, outperforms existing approaches in precision of extracted entities, especially in the recognition of compound terms such as German Federal Ministry of Education and Research and inflected terms.
