@InProceedings{Siddig2025_1260,
<br/>   author = {Ibrahim Siddig and Sviatoslav Tugeev and Munir Georges},
<br/>   booktitle = {Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2025},
<br/>   title = {Pattern-based parsing of German traffic regulations (StVO) for legal knowledge graph construction},
<br/>   year = {2025},
<br/>   editor = {Sven Grawunder},
<br/>   month = mar,
<br/>   pages = {255--264},
<br/>   publisher = {TUDpress, Dresden},
<br/>   abstract  = {: In this work, we present a domain-specific pattern-based parser (Π) designed to extract entities and relationships from the German traffic regulations legal
text (StVO) using explicit citations and hierarchies within the text to construct a
legal knowledge graph (KG). The domain knowledge embedded within the constructed knowledge graph (KG) is a valuable asset for a Large Language Model
(LLM) training as part of future research in this domain. To the best of our knowledge, there has been no specific focus in the literature on parsing and tagging legal
entities and their relationships for constructing knowledge graphs (KGs) from the
German traffic regulations legal text, Straßenverkehrs-Ordnung (StVO), while
focusing specifically on the hierarchical structure of the document. While other
methods of extracting legal and factual knowledge from similar documents have
been attempted, they either focused on different methodologies or different domains. The results indicate that the parser is capable of constructing a highly interconnected graph while maintaining the integrity of the original StraßenverkehrsOrdnung (StVO) legal text, despite the graph’s complexity.
},
<br/>   isbn = {978-3-95908-803-9},
<br/>   issn = {0940-6832},
<br/>   keywords = {Poster},
<br/>   url = {https://www.essv.de/pdf/2025_255_264.pdf},
<br/>}