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Knowledge Extraction for Art History: the Case of Vasari’s The Lives of The Artists (1568)

2022, Santini, Cristian, Tan, Mary Ann, Tietz, Tabea, Bruns, Oleksandra, Posthumus, Etienne, Sack, Harald, Paschke, Adrian, Rehm, Georg, Neudecker, Clemens, Pintscher, Lydia

Knowledge Extraction (KE) techniques are used to convert unstructured information present in texts to Knowledge Graphs (KGs) which can be queried and explored. Despite their potential for cultural heritage domains, such as Art History, these techniques often encounter limitations if applied to domain-specific data. In this paper we present the main challenges that KE has to face on art-historical texts, by using as case study Giorgio Vasari's The Lives of The Artists. This paper discusses the following NLP tasks for art-historical texts, namely entity recognition and linking, coreference resolution, time extraction, motif extraction and artwork extraction. Several strategies to annotate art-historical data for these tasks and evaluate NLP models are also proposed.

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Advances in Semantics and Explainability for NLP: Joint Proceedings of the 2nd International Workshop on Deep Learning meets Ontologies and Natural Language Processing (DeepOntoNLP 2021) & 6th International Workshop on Explainable Sentiment Mining and Emotion Detection (X-SENTIMENT 2021), co-located with the 18th Extended Semantic Web Conference (ESWC 2021)

2021, Ben Abbès, Sarra, Hantach, Rim, Calvez, Philippe, Buscaldi, Davide, Dessì, Danilo, Dragoni, Mauro, Reforgiato Recupero, Diego, Sack, Harald

[no abstract available]