mlsanityData to 7 Oct 2026

KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features

arXiv 1709.04676 ↗First posted 14 Sep 2017cs.AIcited by 25

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Abstract (arXiv)

We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features. We show that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. We contribute a novel data sets enriching commonly used knowledge base completion benchmarks with numerical features. The data sets are available under a permissive BSD-3 license. We also investigate the impact numerical features have on the KB completion performance of KBLRN.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Sep 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 4; 2023: 7; 2024: 4; 2025: 3; 2026: 3.

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  1. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  2. 2022Multi-Modal Knowledge Graph Construction and Application: A Surveycited by 74
  3. 2024NativE: Multi-modal Knowledge Graph Completion in the Wildcited by 23
  4. 2023Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformerscited by 16
  5. 2025Top Ten Challenges Towards Agentic Neural Graph Databasescited by 13
  6. 2023Knowledge Graph Reasoning over Entities and Numerical Valuescited by 11
  7. 2021Learning Embeddings from Knowledge Graphs With Numeric Edge Attributescited by 10
  8. 2021HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrationscited by 9
  9. 2023Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Predictioncited by 9
  10. 2022Augmenting Knowledge Graphs for Better Link Predictioncited by 7
  11. 2023LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literalscited by 6
  12. 2022Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Datacited by 4
13 more
  1. 2023Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphscited by 4
  2. 2023ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completioncited by 2
  3. 2024Numerical Literals in Link Prediction: A Critical Examination of Models and Datasetscited by 2
  4. 2025ReaLitE: Enrichment of Relation Embeddings in Knowledge Graphs using Numeric Literalscited by 2
  5. 2021Transductive Data Augmentation with Relational Path Rule Mining for Knowledge Graph Embeddingcited by 1
  6. 2024Wikidata as a seed for Web Extractioncited by 1
  7. 2024NumCoKE: Ordinal-Aware Numerical Reasoning over Knowledge Graphs with Mixture-of-Experts and Contrastive Learningcited by 1
  8. 2026Return of the Schema: Building Complete Datasets for Machine Learning and Reasoning on Knowledge Graphscited by 1
  9. 2021ProcK: Machine Learning for Knowledge-Intensive Processescited by 0
  10. 2023On the Aggregation of Rules for Knowledge Graph Completioncited by 0
  11. 2025FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphscited by 0
  12. 2026Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completioncited by 0
  13. 2026Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphscited by 0