Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings

Zahera, Hamada M.; Heindorf, Stefan; Balke, Stefan; Haupt, Jonas; Voigt, Martin; Walter, Carolin; Witter, Fabian; Ngomo, Axel-Cyrille Ngonga

doi:10.1007/978-3-031-11609-4_9

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2022

2024

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Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Scholarly Publications: Our framework have been effectively used to learn knowledge graphs embeddings in several published works [3,4,[13][14][15][16], and [17].…”

Section: Software Impactmentioning

confidence: 99%

Hardware-agnostic computation for large-scale knowledge graph embeddings

Demir

Ngomo²

2022

Software Impacts

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“…Scholarly Publications: Our framework have been effectively used to learn knowledge graphs embeddings in several published works [3,4,[13][14][15][16], and [17].…”

Section: Software Impactmentioning

confidence: 99%

Hardware-agnostic computation for large-scale knowledge graph embeddings

Demir

Ngomo²

2022

Software Impacts

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“…Scholarly Publications: Our framework have been effectively used to learn knowledge graphs embeddings in several published works [13], [3], [14], [4], [15], [16], and [17].…”

Section: Software Impactmentioning

confidence: 99%

Hardware-agnostic Computation for Large-scale Knowledge Graph Embeddings

Demir¹,

Ngomo²

2022

Preprint

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Knowledge graph embedding research has mainly focused on learning continuous representations of knowledge graphs towards the link prediction problem. Recently developed frameworks can be effectively applied in research related applications. Yet, these frameworks do not fulfill many requirements of real-world applications. As the size of the knowledge graph grows, moving computation from a commodity computer to a cluster of computers in these frameworks becomes more challenging. Finding suitable hyperparameter settings w.r.t. time and computational budgets are left to practitioners. In addition, the continual learning aspect in knowledge graph embedding frameworks is often ignored, although continual learning plays an important role in many real-world (deep) learning-driven applications. Arguably, these limitations explain the lack of publicly available knowledge graph embedding models for large knowledge graphs. We developed a framework based on the frameworks DASK, Pytorch Lightning and Hugging Face to compute embeddings for large-scale knowledge graphs in a hardware-agnostic manner, which is able to address real-world challenges pertaining to the scale of real application. We provide an open-source version of our framework along with a hub of pre-trained models having more than 11.4 B parameters 2 .

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Anomalies resolution and semantification of tabular data

Sharma,

Jain

2024

J Supercomput

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Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings

Cited by 3 publications

References 7 publications

Hardware-agnostic computation for large-scale knowledge graph embeddings

Hardware-agnostic computation for large-scale knowledge graph embeddings

Hardware-agnostic Computation for Large-scale Knowledge Graph Embeddings

Anomalies resolution and semantification of tabular data

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