# KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction

arXiv: https://arxiv.org/abs/2106.00459 · HTML: https://mlsanity.com/paper/2106.00459/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 43 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://mlsanity.com/paper/1704.02901/) (2017)
- [Graph Convolution over Pruned Dependency Trees Improves Relation Extraction](https://mlsanity.com/paper/1809.10185/) (2018)
- [Towards Sparse Hierarchical Graph Classifiers](https://mlsanity.com/paper/1811.01287/) (2018)
- [RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information](https://mlsanity.com/paper/1812.04361/) (2018)
- [Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification](https://mlsanity.com/paper/1711.05859/) (2017)
- [Classifying Relations by Ranking with Convolutional Neural Networks](https://mlsanity.com/paper/1504.06580/) (2015)
- [RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network](https://mlsanity.com/paper/2009.08694/) (2020)
- [Graph Neural Networks with Generated Parameters for Relation Extraction](https://mlsanity.com/paper/1902.00756/) (2019)
- [Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions](https://mlsanity.com/paper/1904.00143/) (2019)
- [Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning](https://mlsanity.com/paper/2010.04314/) (2020)
- [Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models](https://mlsanity.com/paper/2008.05190/) (2020)
- [CokeBERT: Contextual Knowledge Selection and Embedding towards Enhanced Pre-Trained Language Models](https://mlsanity.com/paper/2009.13964/) (2020)
- [Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph](https://mlsanity.com/paper/2004.11529/) (2020)
- [Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction](https://mlsanity.com/paper/1903.10126/) (2019)
- [Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector](https://mlsanity.com/paper/1811.05616/) (2018)

## Built on by (most cited first, 12 of 12)

- [Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2204.07321/) (2022)
- [Beyond modeling: NLP Pipeline for efficient environmental policy analysis](https://mlsanity.com/paper/2201.07105/) (2022)
- [ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction](https://mlsanity.com/paper/2309.01370/) (2023)
- [Question Answering with Deep Neural Networks for Semi-Structured Heterogeneous Genealogical Knowledge Graphs](https://mlsanity.com/paper/2307.16214/) (2023)
- [Knowledge-Driven Cross-Document Relation Extraction](https://mlsanity.com/paper/2405.13546/) (2024)
- [DepNeCTI: Dependency-based Nested Compound Type Identification for Sanskrit](https://mlsanity.com/paper/2310.09501/) (2023)
- [Relation Extraction Across Entire Books to Reconstruct Community Networks: The AffilKG Datasets](https://mlsanity.com/paper/2505.10798/) (2025)
- [Knowledge-Enhanced Relation Extraction Dataset](https://mlsanity.com/paper/2210.11231/) (2022)
- [Noise in Relation Classification Dataset TACRED: Characterization and Reduction](https://mlsanity.com/paper/2311.12298/) (2023)
- [Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction](https://mlsanity.com/paper/2312.11062/) (2023)
- [SCoRE: Streamlined Corpus-based Relation Extraction using Multi-Label Contrastive Learning and Bayesian kNN](https://mlsanity.com/paper/2507.06895/) (2025)
- [Re-Representation in Sentential Relation Extraction with Sequence Routing Algorithm](https://mlsanity.com/paper/2508.21049/) (2025)

## Abstract (arXiv)

We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG). Especially in this presumed sentential RE setting, the context of a single sentence is often sparse. This paper introduces the KGPool method to address this sparsity, dynamically expanding the context with additional facts from the KG. It learns the representation of these facts (entity alias, entity descriptions, etc.) using neural methods, supplementing the sentential context. Unlike existing methods that statically use all expanded facts, KGPool conditions this expansion on the sentence. We study the efficacy of KGPool by evaluating it with different neural models and KGs (Wikidata and NYT Freebase). Our experimental evaluation on standard datasets shows that by feeding the KGPool representation into a Graph Neural Network, the overall method is significantly more accurate than state-of-the-art methods.