At the intersection of computer science and Humanities, our research enhances the understanding of oral historians’ hermeneutic interpretation and argumentation workflow. We utilize transcripts of audiovisual interviews with contemporary witnesses as the foundation of our research. This paper focuses on supporting hermeneutic interpretation and argumentation by exploring semantic search within machine-readable arguments. Machine-readable arguments are an experimental method in various other research fields, e.g., medicine. Until now, the application is limited to natural language, which holds grammatically complete structures. Qualitative interviews, by contrast, exist as highly unstructured data. Our approach first employs a manual workflow to derive RDF-encoded machine-readable arguments, carrying the potential to utilize the formalization in a semi-automated or automated manner in future applications. Our work supports both grammatically complete and grammatically incomplete arguments, allowing us to reflect all kinds of arguments from those found in the raw text using incomplete grammar to analytical and interpretive annotations by Information Visualization (IVIS). We address the issue of formalizing arguments to extend the machine-readability of argument structures by their semantics. Thus, our approach extends a Knowledge-Management System (KMS) by implementing an argument storage application for hermeneutic research. Employing our approach enables researchers to distinguish subjects or objections in statements when searching for relevant material in the KMS. Furthermore, the standardization of arguments facilitates the visualization of arguments. Fields of application for argumentation structure encompass IVIS and collaboration on argumentation. Similarly, KMS incorporating our approach supports researchers in handling ever-growing quantities of research materials.