mlsanityData to 7 Oct 2026

μKG: A Library for Multi-source Knowledge Graph Embeddings and Applications

arXiv 2207.11442 ↗First posted 23 Jul 2022cs.CL cs.AI cs.LGcited by 4

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Abstract (arXiv)

This paper presents μKG, an open-source Python library for representation learning over knowledge graphs. μKG 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. μKG provides advanced implementations of embedding techniques with simplified pipelines of different tasks. It also comes with high-quality documentation for ease of use. μKG 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 μKG.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 34 of its 58 references that are in the map. First posted on arXiv 23 Jul 2022.

Builds on

34 of 58 references.

References · 34

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22 more
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Cited by year

Citing papers per year: 2023: 4.

4 papers.

  1. 2023Joint Pre-training and Local Re-training: Transferable Representation Learning on Multi-source Knowledge Graphscited by 5
  2. 2023Knowledge Graph Error Detection with Contrastive Confidence Adaptioncited by 4
  3. 2023Two is Better Than One: Answering Complex Questions by Multiple Knowledge Sources with Generalized Linkscited by 2
  4. 2023NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learningcited by 1