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\(\mu \text {KG}\): A Library for Multi-source Knowledge Graph Embeddings and Applications

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The Semantic Web – ISWC 2022 (ISWC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13489))

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Abstract

This paper presents \(\mu \text {KG}\), an open-source Python library for representation learning over knowledge graphs. \(\mu \text {KG}\) supports joint representation learning over multi-source knowledge graphs (and also a single knowledge graph), multiple deep learning libraries (PyTorch and TensorFlow2), multiple embedding tasks (link prediction, entity alignment, entity typing, and multi-source link prediction), and multiple parallel computing modes (multi-process and multi-GPU computing). It currently implements 26 popular knowledge graph embedding models and supports 16 benchmark datasets. \(\mu \text {KG}\) provides advanced implementations of embedding techniques with simplified pipelines of different tasks. It also comes with high-quality documentation for ease of use. \(\mu \text {KG}\) is more comprehensive than existing knowledge graph embedding libraries. It is useful for a thorough comparison and analysis of various embedding models and tasks. We show that the jointly learned embeddings can greatly help knowledge-powered downstream tasks, such as multi-hop knowledge graph question answering. We will stay abreast of the latest developments in the related fields and incorporate them into \(\mu \text {KG}\).

Resource Type: Software

License: GPL-3.0 License

GitHub Repository: https://github.com/nju-websoft/muKG

X. Luo and Z. Sun—Equal contributors.

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Notes

  1. 1.

    https://www.ray.io/.

  2. 2.

    https://github.com/nju-websoft/muKG.

  3. 3.

    We use uniform negative sampling for a fair comparison with other models.

  4. 4.

    Hereafter, \( ||\cdot || \) denotes the \( L_2 \) vector norm.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (No. 61872172), Beijing Academy of Artificial Intelligence (BAAI), and Collaborative Innovation Center of Novel Software Technology & Industrialization. Zequn Sun was also grateful for the support of Program A for Outstanding PhD Candidates of Nanjing University.

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Authors and Affiliations

  1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Xindi Luo, Zequn Sun & Wei Hu

  2. National Institute of Healthcare Data Science, Nanjing University, Nanjing, China

    Wei Hu

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  1. Xindi Luo
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  2. Zequn Sun
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  3. Wei Hu
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Correspondence to Wei Hu .

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Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Ulrike Sattler

  2. University of Chile, Santiago, Chile

    Aidan Hogan

  3. University of Cape Town, Cape Town, South Africa

    Maria Keet

  4. University of Bologna, Bologna, Italy

    Valentina Presutti

  5. Universidade Federal do Espírito Santo, Vitória, Brazil

    João Paulo A. Almeida

  6. National Institute of Informatics, Tokyo, Japan

    Hideaki Takeda

  7. Orange, Belfort, France

    Pierre Monnin

  8. Sapienza University of Rome, Rome, Italy

    Giuseppe Pirrò

  9. University of Bari, Bari, Italy

    Claudia d’Amato

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Luo, X., Sun, Z., Hu, W. (2022). \(\mu \text {KG}\): A Library for Multi-source Knowledge Graph Embeddings and Applications. In: Sattler, U., et al. The Semantic Web – ISWC 2022. ISWC 2022. Lecture Notes in Computer Science, vol 13489. Springer, Cham. https://doi.org/10.1007/978-3-031-19433-7_35

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  • DOI: https://doi.org/10.1007/978-3-031-19433-7_35

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