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

arXiv: https://arxiv.org/abs/1709.04676 · HTML: https://mlsanity.com/paper/1709.04676/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2017.

## Built on by (most cited first, 26 of 26)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Multi-Modal Knowledge Graph Construction and Application: A Survey](https://mlsanity.com/paper/2202.05786/) (2022)
- [NativE: Multi-modal Knowledge Graph Completion in the Wild](https://mlsanity.com/paper/2406.17605/) (2024)
- [Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers](https://mlsanity.com/paper/2305.18256/) (2023)
- [Top Ten Challenges Towards Agentic Neural Graph Databases](https://mlsanity.com/paper/2501.14224/) (2025)
- [Knowledge Graph Reasoning over Entities and Numerical Values](https://mlsanity.com/paper/2306.01399/) (2023)
- [Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes](https://mlsanity.com/paper/2105.08683/) (2021)
- [HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations](https://mlsanity.com/paper/2108.05774/) (2021)
- [Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction](https://mlsanity.com/paper/2302.02601/) (2023)
- [Augmenting Knowledge Graphs for Better Link Prediction](https://mlsanity.com/paper/2203.13965/) (2022)
- [LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals](https://mlsanity.com/paper/2304.14742/) (2023)
- [Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data](https://mlsanity.com/paper/2201.08580/) (2022)
- [Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs](https://mlsanity.com/paper/2401.00608/) (2023)
- [ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion](https://mlsanity.com/paper/2312.07589/) (2023)
- [Numerical Literals in Link Prediction: A Critical Examination of Models and Datasets](https://mlsanity.com/paper/2407.18241/) (2024)
- [ReaLitE: Enrichment of Relation Embeddings in Knowledge Graphs using Numeric Literals](https://mlsanity.com/paper/2504.00852/) (2025)
- [Transductive Data Augmentation with Relational Path Rule Mining for Knowledge Graph Embedding](https://mlsanity.com/paper/2111.00974/) (2021)
- [Wikidata as a seed for Web Extraction](https://mlsanity.com/paper/2401.07812/) (2024)
- [NumCoKE: Ordinal-Aware Numerical Reasoning over Knowledge Graphs with Mixture-of-Experts and Contrastive Learning](https://mlsanity.com/paper/2411.12950/) (2024)
- [Return of the Schema: Building Complete Datasets for Machine Learning and Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2602.14795/) (2026)
- [ProcK: Machine Learning for Knowledge-Intensive Processes](https://mlsanity.com/paper/2109.04881/) (2021)
- [On the Aggregation of Rules for Knowledge Graph Completion](https://mlsanity.com/paper/2309.00306/) (2023)
- [FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs](https://mlsanity.com/paper/2510.00894/) (2025)
- [Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion](https://mlsanity.com/paper/2607.26909/) (2026)
- [Neural Regression with Embeddings for Numerical Attribute Prediction in Knowledge Graphs](https://mlsanity.com/paper/2608.26729/) (2026)
- [Silence and Noise: Resolving Suppression and Overvaluation in Multimodal Entity Alignment](https://mlsanity.com/paper/2610.16106/) (2026)

## 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.