@inproceedings{schleider-troncy-2021-zero,
title = "Zero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles",
author = "Schleider, Thomas and
Troncy, Raphael",
editor = "Degaetano-Ortlieb, Stefania and
Kazantseva, Anna and
Reiter, Nils and
Szpakowicz, Stan",
booktitle = "Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic (online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.latechclfl-1.16",
doi = "10.18653/v1/2021.latechclfl-1.16",
pages = "138--146",
abstract = "The knowledge of the European silk textile production is a typical case for which the information collected is heterogeneous, spread across many museums and sparse since rarely complete. Knowledge Graphs for this cultural heritage domain, when being developed with appropriate ontologies and vocabularies, enable to integrate and reconcile this diverse information. However, many of these original museum records still have some metadata gaps. In this paper, we present a zero-shot learning approach that leverages the ConceptNet common sense knowledge graph to predict categorical metadata informing about the silk objects production. We compared the performance of our approach with traditional supervised deep learning-based methods that do require training data. We demonstrate promising and competitive performance for similar datasets and circumstances and the ability to predict sometimes more fine-grained information. Our results can be reproduced using the code and datasets published at \url{https://github.com/silknow/ZSL-KG-silk}.",
}
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%0 Conference Proceedings
%T Zero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles
%A Schleider, Thomas
%A Troncy, Raphael
%Y Degaetano-Ortlieb, Stefania
%Y Kazantseva, Anna
%Y Reiter, Nils
%Y Szpakowicz, Stan
%S Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic (online)
%F schleider-troncy-2021-zero
%X The knowledge of the European silk textile production is a typical case for which the information collected is heterogeneous, spread across many museums and sparse since rarely complete. Knowledge Graphs for this cultural heritage domain, when being developed with appropriate ontologies and vocabularies, enable to integrate and reconcile this diverse information. However, many of these original museum records still have some metadata gaps. In this paper, we present a zero-shot learning approach that leverages the ConceptNet common sense knowledge graph to predict categorical metadata informing about the silk objects production. We compared the performance of our approach with traditional supervised deep learning-based methods that do require training data. We demonstrate promising and competitive performance for similar datasets and circumstances and the ability to predict sometimes more fine-grained information. Our results can be reproduced using the code and datasets published at https://github.com/silknow/ZSL-KG-silk.
%R 10.18653/v1/2021.latechclfl-1.16
%U https://aclanthology.org/2021.latechclfl-1.16
%U https://doi.org/10.18653/v1/2021.latechclfl-1.16
%P 138-146
Markdown (Informal)
[Zero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles](https://aclanthology.org/2021.latechclfl-1.16) (Schleider & Troncy, LaTeCHCLfL 2021)
ACL