The Odeuropa project integrated expertise in sensory mining, knowledge representation, computational linguistics, (art) history, and heritage science. Digital data were extracted from thousands of images and historical texts in six languages, all available in the public domain through the Smell Explorer or the Encyclopaedia of Smell History and Heritage.
Digital textual collections integrated into the workflow are represented in the Knowledge Graph, including eighteen GLAM institutions. Among others, these institutions include the British Library, Digitale Bibliotheek voor de Nederlandse Letteren, Deutsches Text Archiv, Digital Library of Slovenia, Europeana, Gallica, and WikiSource. Cooperating GLAMs collaborated with researchers from the UK, Netherlands, Germany, Italy, France, and Slovenia. In this paper, the results of inter- and transdisciplinary work are presented.
A variety of smell experiences, described by smell words, smell sources, qualities associated with smells, smell perceivers, etc., have been extracted from the available digital data. Books (monographs and dissertations) and periodicals (historical, scientific, general newspapers, and journals) have been the primary resources for Odeuropa smell data analysis. Additionally, manuscripts (mediaeval codices and literary manuscripts); images (photographs, postcards, posters); music (musical scores and audio recordings); and maps (maps and atlases) were used.
The Smell Experiences extraction workflow applied in the Odeuropa project encompassed six main steps: (1) Development of annotated benchmarks, (2) Analysis of benchmark statistics, (3) Development of a text processing system, (4) Extraction of Smell experiences, (5) Linking with Odeuropa semantic vocabularies, and (6) Exploration, visualization, storytelling. The workflow enabled the integration of a wide variety of resources for digital humanities research, from textual materials to visual representations.
The Odeuropa project integrated expertise in sensory mining, knowledge representation, computational linguistics, (art) history, and heritage science. Digital data were extracted from thousands of images and historical texts in six languages, all available in the public domain through the Smell Explorer or the Encyclopaedia of Smell History and Heritage.
Digital textual collections integrated into the workflow are represented in the Knowledge Graph, including eighteen GLAM institutions. Among others, these institutions include the British Library, Digitale Bibliotheek voor de Nederlandse Letteren, Deutsches Text Archiv, Digital Library of Slovenia, Europeana, Gallica, and WikiSource. Cooperating GLAMs collaborated with researchers from the UK, Netherlands, Germany, Italy, France, and Slovenia. In this paper, the results of inter- and transdisciplinary work are presented.
A variety of smell experiences, described by smell words, smell sources, qualities associated with smells, smell perceivers, etc., have been extracted from the available digital data. Books (monographs and dissertations) and periodicals (historical, scientific, general newspapers, and journals) have been the primary resources for Odeuropa smell data analysis. Additionally, manuscripts (mediaeval codices and literary manuscripts); images (photographs, postcards, posters); music (musical scores and audio recordings); and maps (maps and atlases) were used.
The Smell Experiences extraction workflow applied in the Odeuropa project encompassed six main steps: (1) Development of annotated benchmarks, (2) Analysis of benchmark statistics, (3) Development of a text processing system, (4) Extraction of Smell experiences, (5) Linking with Odeuropa semantic vocabularies, and (6) Exploration, visualization, storytelling. The workflow enabled the integration of a wide variety of resources for digital humanities research, from textual materials to visual representations.
Type:
Poster / Demo
City:
Lisbon
Date:
2024-06-18
Department:
Data Science
Eurecom Ref:
7787
Copyright:
© EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in and is available at :
See also:
