Semantic Medieval Gis
information extraction and machine learning
Information extraction and machine learning for medieval data science: entity profiling, knowledge graph building and geo-linking
Info: In different projects the data of the Regesta Imperii were analyzed from a computational linguistic point of view.
- Topic 1: Deriving Players & Themes in the Regesta Imperii using SVMs and Neural Networks. (2016)
- Topic 2: Knowledge Graph of the Regesta Imperii. (2018-)
- Topic 3: Creating a Semantic Medieval GIS from the Regesta Imperii. (2019-)
People: Main responsibility: Juri Opitz
Persons: Leo Born, Yannick Pultar, Anette Frank, Vivi Nastase
Timeframe: since 2016
Links: Github
publications:
- Juri Opitz: Automatic Creation of a Large-Scale Tempo-Spatial and Semantic Medieval European Information System. In: Proceedings of the Workshop on Computational Humanities Research (CHR). Amsterdam 2020, S. 397-419 http://ceur-ws.org/Vol-2723/long12.pdf.
- Juri Opitz, Anette Frank: Deriving Players & Themes in the Regesta Imperii using SVMs and Neural Networks. In: Proceedings of the 10th {SIGHUM Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. Berlin 2016, S. 74-83 http://dx.doi.org/10.18653/v1/W16-2108.
- Juri Opitz, Leo Born, Vivi Nastase: Induction of a Large-Scale Knowledge Graph from the Regesta Imperii. In: Proceedings of the Second Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. Santa Fe 2018, S. 159-168 https://aclanthology.org/W18-4518.
- Leo Born, Juri Opitz, Vivi Nastase: A Knowledge Graph from the Regesta Imperii: Construction, Visualization and Macro-level Analyses. In: Inaugural Conference of the European Association for Digital Humanities (EADH). Galway 2018.
- Opitz, Juri; Born, Leo; Nastase, Vivi; Pultar, Yannick: Automatic Reconstruction of Emperor Itineraries from the Regesta Imperii, in: Proceedings of the 3rd International Conference on Digital Access to Textual Cultural Heritage (DATeCH) - New York (2019), S. 39-44 https://doi.org/10.1145/3322905.3322921.
Status: Active