# Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction

arXiv: https://arxiv.org/abs/1307.7973 · HTML: https://mlsanity.com/paper/1307.7973/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2013.

## Built on by (most cited first, 21 of 21)

- [NormBank: A Knowledge Bank of Situational Social Norms](https://mlsanity.com/paper/2305.17008/) (2023)
- [Learning Concept Lengths Accelerates Concept Learning in ALC](https://mlsanity.com/paper/2107.04911/) (2021)
- [Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors](https://mlsanity.com/paper/2104.08225/) (2021)
- [Multi-view Inference for Relation Extraction with Uncertain Knowledge](https://mlsanity.com/paper/2104.13579/) (2021)
- [Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models](https://mlsanity.com/paper/2305.12544/) (2023)
- [Neural Class Expression Synthesis](https://mlsanity.com/paper/2111.08486/) (2021)
- [From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models](https://mlsanity.com/paper/2411.05036/) (2024)
- [A Question-answering Based Framework for Relation Extraction Validation](https://mlsanity.com/paper/2104.02934/) (2021)
- [CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction](https://mlsanity.com/paper/2106.00793/) (2021)
- [Towards Robust Knowledge Graph Embedding via Multi-task Reinforcement Learning](https://mlsanity.com/paper/2111.06103/) (2021)
- [Understanding Substructures in Commonsense Relations in ConceptNet](https://mlsanity.com/paper/2210.01263/) (2022)
- [Federated Neuro-Symbolic Learning](https://mlsanity.com/paper/2308.15324/) (2023)
- [On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion](https://mlsanity.com/paper/2504.08970/) (2025)
- [Question-Aware Memory Network for Multi-hop Question Answering in Human-Robot Interaction](https://mlsanity.com/paper/2104.13173/) (2021)
- [Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2211.01432/) (2022)
- [Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation](https://mlsanity.com/paper/2307.15776/) (2023)
- [Multimodal Cultural Heritage Knowledge Graph Extension with Language and Vision Models](https://mlsanity.com/paper/2605.17669/) (2026)
- [Budget Sensitive Reannotation of Noisy Relation Classification Data Using Label Hierarchy](https://mlsanity.com/paper/2112.13320/) (2021)
- [Learning to Borrow -- Relation Representation for Without-Mention Entity-Pairs for Knowledge Graph Completion](https://mlsanity.com/paper/2204.13097/) (2022)
- [V-Coder: Adaptive AutoEncoder for Semantic Disclosure in Knowledge Graphs](https://mlsanity.com/paper/2208.01735/) (2022)
- [Exploring Embeddings for Measuring Text Relatedness: Unveiling Sentiments and Relationships in Online Comments](https://mlsanity.com/paper/2310.05964/) (2023)

## Abstract (arXiv)

This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on two scoring functions that operate by learning low-dimensional embeddings of words and of entities and relationships from a knowledge base. We empirically show on New York Times articles aligned with Freebase relations that our approach is able to efficiently use the extra information provided by a large subset of Freebase data (4M entities, 23k relationships) to improve over existing methods that rely on text features alone.