mlsanityData to 14 Oct 2026

MEKER: Memory Efficient Knowledge Embedding Representation for Link Prediction and Question Answering

arXiv 2204.10629 ↗First posted 22 Apr 2022cs.CL cs.AI cs.IRcited by 4

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Abstract (arXiv)

Knowledge Graphs (KGs) are symbolically structured storages of facts. The KG embedding contains concise data used in NLP tasks requiring implicit information about the real world. Furthermore, the size of KGs that may be useful in actual NLP assignments is enormous, and creating embedding over it has memory cost issues. We represent KG as a 3rd-order binary tensor and move beyond the standard CP decomposition by using a data-specific generalized version of it. The generalization of the standard CP-ALS algorithm allows obtaining optimization gradients without a backpropagation mechanism. It reduces the memory needed in training while providing computational benefits. We propose a MEKER, a memory-efficient KG embedding model, which yields SOTA-comparable performance on link prediction tasks and KG-based Question Answering.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 38 references that are in the map. First posted on arXiv 22 Apr 2022.

Builds on

17 of 38 references.

References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
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  11. 2017A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Networkcited by 143
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5 more
  1. 2019Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphscited by 125
  2. 2019PyTorch-BigGraph: A Large-scale Graph Embedding Systemcited by 119
  3. 2017Knowledge Graph Completion via Complex Tensor Factorizationcited by 80
  4. 2018Hypernetwork Knowledge Graph Embeddingscited by 38
  5. 2018Generalized Canonical Polyadic Tensor Decompositioncited by 34

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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 2.

4 papers.

  1. 2023GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answeringcited by 6
  2. 2025Knowledge Graph Completion with Mixed Geometry Tensor Factorizationcited by 3
  3. 2025The benefits of query-based KGQA systems for complex and temporal questions in LLM eracited by 1
  4. 2023Node-based Knowledge Graph Contrastive Learning for Medical Relationship Predictioncited by 0