arXiv:2610.28648v1 Announce Type: cross Abstract: Academic theses are a valuable source of scientific knowledge, yet their semantic exploitation remains limited. While Scholarly Knowledge Graphs have significantly advanced the representation of scientific publications, knowledge graph construction for academic theses has received comparatively limited attention. This paper provides a systematic review of existing approaches and analyzes their potential application for the thesis domain. We analyzed 46 peer-reviewed studies published between 2021 and 2025, and organised existing approaches into a structured classification of techniques, models, and tools. The review reveals a methodological evolution from natural language processing techniques for entity and relation extraction to ontology engineering, Semantic Web technologies, and AI-driven enrichment through knowledge graph embeddings, Linked Open Data, and Large Language Models. The findings provide a consolidated view of the current state of the art and identify methodological foundations and future research directions for building knowledge graphs for the thesis domain.