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  4. Ontology-based entity recognition and annotation
 
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2020
Konferenzbeitrag
Titel

Ontology-based entity recognition and annotation

Abstract
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.
Author(s)
Hoppe, Thomas
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Al Qundus, Jamal
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Peikert, Silvio
Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS
Hauptwerk
Qurator 2020. Conference on Digital Curation Technologies. Online resource
Project(s)
Qurator
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Konferenz
International Conference on Digital Curation Technologies (Qurator) 2020
File(s)
N-575026.pdf (589 KB)
Language
Englisch
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FOKUS
Tags
  • Ontology

  • Complex Entity Recogn...

  • Text Annotation

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