Data

MELArt: Multimodal Entity Linking Evaluation Dataset for Art (Version 1.0)

The University of Queensland
Associate Professor Gianluca Demartini (Aggregated by) Associate Professor Gianluca Demartini (Aggregated by) Dr Thai Linh Le (Aggregated by) Ms Thai Linh Le (Aggregated by)
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.48610/2a8ef30&rft.title=MELArt: Multimodal Entity Linking Evaluation Dataset for Art (Version 1.0)&rft.identifier=RDM ID: 51211f90-9558-11ee-b5f6-0970a7bfad05&rft.publisher=The University of Queensland&rft.description=This dataset encapsulates various entities unique to art, presenting a robust tool tailored to the creation of multi-modal art datasets. We also apply distinct metrics that assess ambiguity and diversity in the data, offering a thorough evaluation of our dataset. We introduce an automated process that facilitates the generation of art datasets, harnessing data from multiple sources (ArtPedia, Wikidata and Wikimedia Commons) to ensure reliability and comprehensiveness. Furthermore, our paper provides extensive statistical data, delineates best practices for the integration of art datasets, and presents a detailed performance analysis of entity linking systems, when applied to domain-specific datasets.&rft.creator=Associate Professor Gianluca Demartini&rft.creator=Associate Professor Gianluca Demartini&rft.creator=Dr Thai Linh Le&rft.creator=Ms Thai Linh Le&rft.date=2023&rft_rights= https://guides.library.uq.edu.au/deposit-your-data/license-reuse-data-agreement&rft_subject=eng&rft_subject=Data management and data science&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft.type=dataset&rft.language=English Access the data
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g.demartini@uq.edu.au
School of Electrical Engineering and Computer Science

Full description

This dataset encapsulates various entities unique to art, presenting a robust tool tailored to the creation of multi-modal art datasets. We also apply distinct metrics that assess ambiguity and diversity in the data, offering a thorough evaluation of our dataset. We introduce an automated process that facilitates the generation of art datasets, harnessing data from multiple sources (ArtPedia, Wikidata and Wikimedia Commons) to ensure reliability and comprehensiveness. Furthermore, our paper provides extensive statistical data, delineates best practices for the integration of art datasets, and presents a detailed performance analysis of entity linking systems, when applied to domain-specific datasets.

Issued: 2023

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Subjects
Data Management and Data Science | Information and Computing Sciences | eng |

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local : UQ:289097

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