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Utilizing emerging knowledge to support medical argument retrieval

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De Gruyter


This article summarizes selected aspects of a dissertation project and prior publications related to the DFG-funded RecomRatio research project. As such, it provides an end-to-end overview of a research project that aims at extracting and utilizing Emerging Knowledge represented by two concepts that we define as Emerging Named Entities and Emerging Argument Entities to support medical argumentation retrieval. We use these two concepts to model novelty in general scientific literature and, in particular, in medical argumentation. Therefore, this paper will provide an overview of Emerging Knowledge and definitions of Emerging Named Entities and Emerging Argument Entities. It includes a review of state-of-the-art and related work. A preparatory study shows that Emerging Argument Entities are in use in the medical literature. Based on the state of the art review and the preparatory study, a conceptual system design based on Emerging Named Entity Recognition and a state-of-the-art Argumentation Mining framework (ArgumenText) is introduced to extract Emerging Argument Entities from medical literature and make them available for Argument Retrieval. The conceptual system design supports two Argument Retrieval use cases: 1.) Ranking of result sets based on Emerging Argument Entities, and 2.) Highlighting Emerging Argument Entities within result sets. A case study for the extraction and visualization of Emerging Named Entities and Emerging Argument Entities is implemented based on the conceptual design. This proof-of-concept system is used to conduct technical evaluations regarding the Emerging Named Entity Recognition. Furthermore, prior results of an expert-based evaluation are presented. The article finishes with a conclusion and brief outlook of future work, e. g., supporting the Argument Interchange Format.


Nawroth, Christian; Engel, Felix; Hemmje, Matthias (2021): Utilizing emerging knowledge to support medical argument retrieval. it - Information Technology: Vol. 63, No. 1. DOI: 10.1515/itit-2020-0049. Berlin: De Gruyter. PISSN: 2196-7032. pp. 3-15