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In this paper, we present an approach to identify and classify newly emerging medical knowledge within documents of a medical digital library from vocabulary evolution. Furthermore, we introduce the field of Medicine as a hermeneutic discipline and present an analysis method of medical literature documents to retrieve digital argumentative medical evidence by application of Digital Hermeneutics methods. We introduce a hermeneutic medical vocabulary analysis to examine Medical Subject Headings Named Entities in PubMed documents to identify emerging Named Entities. Additionally, a new Natural Language Processing feature “emerging Knowledge Documents” is proposed to classify PubMed documents with new and emerging medical knowledge and evidence, supporting medical experts in their decision-making process. This feature helps identify relevant documents with new medical knowledge and arguments to support medical decisions or weigh arguments within argument structures. Therefore, we extend the PubMed Application Programming Interface schematically by a new endpoint to execute an emerging Knowledge Document classification analysis for a given n Named Entity and PubMed-indexed medical literature document. We present a system design for an emerging Knowledge Document classification system and implement it schematically as well as evaluate the analysis based on two documents and a Named Entity.

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Towards Supporting Digital Hermeneutic Application with Emerging Knowledge and Argumentation Detection Through Vocabulary Evolution Analysis

  • 会议论文

  • 发表日期：

    2026年

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  • Alexander Duttenhöfer,
  • Philippe Tamla,
  • Christian Nawroth,
  • Binh Vu,
  • Ingo Frommholz,
  • Matthias Hemmje

摘要

In this paper, we present an approach to identify and classify newly emerging medical knowledge within documents of a medical digital library from vocabulary evolution. Furthermore, we introduce the field of Medicine as a hermeneutic discipline and present an analysis method of medical literature documents to retrieve digital argumentative medical evidence by application of Digital Hermeneutics methods. We introduce a hermeneutic medical vocabulary analysis to examine Medical Subject Headings Named Entities in PubMed documents to identify emerging Named Entities. Additionally, a new Natural Language Processing feature “emerging Knowledge Documents” is proposed to classify PubMed documents with new and emerging medical knowledge and evidence, supporting medical experts in their decision-making process. This feature helps identify relevant documents with new medical knowledge and arguments to support medical decisions or weigh arguments within argument structures. Therefore, we extend the PubMed Application Programming Interface schematically by a new endpoint to execute an emerging Knowledge Document classification analysis for a given n Named Entity and PubMed-indexed medical literature document. We present a system design for an emerging Knowledge Document classification system and implement it schematically as well as evaluate the analysis based on two documents and a Named Entity.