@inproceedings{10.1145/3587259.3627562, author = {Vafaie, Mahsa and Bruns, Oleksandra and Pilz, Nastasja and Waitelonis, J\"{o}rg and Sack, Harald}, title = {CourtDocs Ontology: Towards a Data Model for Representation of Historical Court Proceedings}, year = {2023}, isbn = {9798400701412}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3587259.3627562}, doi = {10.1145/3587259.3627562}, abstract = {For several decades researchers have studied legal documents for insights into the evolution of legal norms and strategies, in their social and cultural context. Analysing these documents and the associated legislative sessions, trials and court cases helps uncover hidden narratives and patterns, as well as showcase the lessons learnt. The field of knowledge engineering has contributed to the growing interest in the development and use of legal ontologies that aim at providing machine-readable foundations to model legal concepts, relations and processes. Legal ontologies have been used for legal knowledge management and as knowledge bases in legal knowledge systems. With a focus on the Wiedergutmachung project as a use case, this paper presents an overview of the existing legal ontologies, demonstrates the gap to align them with the essential conceptual framework required to model historical court proceedings with respect to provenance information, and presents the ongoing work towards developing the CourtDocs Ontology by utilising existing standards and ontologies on the intersection of the legal domain, history and archival sciences. The Wiedergutmachung project centres around constructing a knowledge graph as a backbone for information systems, based on historical archival records from the compensation procedure in post-World War II Germany.}, booktitle = {Proceedings of the 12th Knowledge Capture Conference 2023}, pages = {175–179}, numpages = {5}, keywords = {Knowledge Representation, Archival Documents, Ontology}, location = {, Pensacola, FL, USA, }, series = {K-CAP '23} }