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VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph

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The Semantic Web (ESWC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14665))

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Abstract

The availability of vast amounts of visual data with diverse and fruitful features is a key factor for developing, verifying, and benchmarking advanced computer vision (CV) algorithms and architectures. Most visual datasets are created and curated for specific tasks or with limited data distribution for very specific fields of interest, and there is no unified approach to manage and access them across diverse sources, tasks, and taxonomies. This not only creates unnecessary overheads when building robust visual recognition systems, but also introduces biases into learning systems and limits the capabilities of data-centric AI. To address these problems, we propose the Vision Knowledge Graph (VisionKG), a novel resource that interlinks, organizes and manages visual datasets via knowledge graphs and Semantic Web technologies. It can serve as a unified framework facilitating simple access and querying of state-of-the-art visual datasets, regardless of their heterogeneous formats and taxonomies. One of the key differences between our approach and existing methods is that VisionKG is not only based on metadata but also utilizes a unified data schema and external knowledge bases to integrate, interlink, and align visual datasets. It enhances the enrichment of the semantic descriptions and interpretation at both image and instance levels and offers data retrieval and exploratory services via SPARQL and natural language empowered by Large Language Models (LLMs). VisionKG currently contains 617 million RDF triples that describe approximately 61 million entities, which can be accessed at https://vision.semkg.org and through APIs. With the integration of 37 datasets and four popular computer vision tasks, we demonstrate its usefulness across various scenarios when working with computer vision pipelines.

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Notes

  1. 1.

    https://huggingface.co/docs/datasets/index.

  2. 2.

    https://github.com/opendatalab/opendatalab-python-sdk.

  3. 3.

    https://paperswithcode.com/datasets.

  4. 4.

    https://vision.semkg.org.

  5. 5.

    https://github.com/cqels/vision.

  6. 6.

    https://vision.semkg.org/sparql.

  7. 7.

    List of dataset licenses in VisionKG: http://vision.semkg.org/licences.html.

  8. 8.

    https://creativecommons.org/licenses/by/4.0/.

  9. 9.

    https://github.com/cqels/vision.

  10. 10.

    https://github.com/cqels/vision.

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Acknowledgements

This work is supported by the Deutsche Forschungsgemeinschaft, German Research Foundation under grant number 453130567 (COSMO), by the Horizon Europe Research and Innovation Actions under grant number 101092908 (SmartEdge), by the Federal Ministry for Education and Research, Germany under grant number 01IS18037A (BIFOLD) and by the Horizon Europe Research and Innovation programme under grant agreement number 101079214 (AIoTwin).

Author information

Authors and Affiliations

  1. Open Distributed Systems, Technical University of Berlin, Berlin, Germany

    Jicheng Yuan, Anh Le-Tuan, Manh Nguyen-Duc, Manfred Hauswirth & Danh Le-Phuoc

  2. Bosch Center for Artificial Intelligence, Renningen, Germany

    Trung-Kien Tran

  3. Fraunhofer Institute for Open Communication Systems, Berlin, Germany

    Manfred Hauswirth & Danh Le-Phuoc

Authors
  1. Jicheng Yuan
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  2. Anh Le-Tuan
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  3. Manh Nguyen-Duc
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  4. Trung-Kien Tran
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  5. Manfred Hauswirth
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  6. Danh Le-Phuoc
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Corresponding author

Correspondence to Jicheng Yuan .

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Editors and Affiliations

  1. King’s College London, London, UK

    Albert Meroño Peñuela

  2. KU Leuven, Sint-Katelijne-Waver, Belgium

    Anastasia Dimou

  3. EURECOM, Biot, France

    Raphaël Troncy

  4. Linköping University, Linköping, Sweden

    Olaf Hartig

  5. Technical University of Munich, Heilbronn, Germany

    Maribel Acosta

  6. Polytechnic Institute of Paris, Palaiseau, France

    Mehwish Alam

  7. University of Mannheim, Mannheim, Germany

    Heiko Paulheim

  8. EURECOM, Biot, France

    Pasquale Lisena

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Yuan, J., Le-Tuan, A., Nguyen-Duc, M., Tran, TK., Hauswirth, M., Le-Phuoc, D. (2024). VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph. In: Meroño Peñuela, A., et al. The Semantic Web. ESWC 2024. Lecture Notes in Computer Science, vol 14665. Springer, Cham. https://doi.org/10.1007/978-3-031-60635-9_5

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