# Incorporating Relation Paths in Neural Relation Extraction

arXiv: https://arxiv.org/abs/1609.07479 · HTML: https://mlsanity.com/paper/1609.07479/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2016.

## Built on by (most cited first, 9 of 9)

- [Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction](https://mlsanity.com/paper/2109.06415/) (2021)
- [Benchmarking Commonsense Knowledge Base Population with an Effective Evaluation Dataset](https://mlsanity.com/paper/2109.07679/) (2021)
- [GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks](https://mlsanity.com/paper/2305.16663/) (2023)
- [Two Training Strategies for Improving Relation Extraction over Universal Graph](https://mlsanity.com/paper/2102.06540/) (2021)
- [Cross-stitching Text and Knowledge Graph Encoders for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2211.01432/) (2022)
- [WHERE and WHICH: Iterative Debate for Biomedical Synthetic Data Augmentation](https://mlsanity.com/paper/2503.23673/) (2025)
- [From Consensus to Disagreement: Multi-Teacher Distillation for Semi-Supervised Relation Extraction](https://mlsanity.com/paper/2112.01048/) (2021)
- [Distantly Supervised Morpho-Syntactic Model for Relation Extraction](https://mlsanity.com/paper/2401.10002/) (2024)
- [Data Transformation Strategies to Remove Heterogeneity](https://mlsanity.com/paper/2507.12677/) (2025)

## Abstract (arXiv)

Distantly supervised relation extraction has been widely used to find novel relational facts from plain text. To predict the relation between a pair of two target entities, existing methods solely rely on those direct sentences containing both entities. In fact, there are also many sentences containing only one of the target entities, which provide rich and useful information for relation extraction. To address this issue, we build inference chains between two target entities via intermediate entities, and propose a path-based neural relation extraction model to encode the relational semantics from both direct sentences and inference chains. Experimental results on real-world datasets show that, our model can make full use of those sentences containing only one target entity, and achieves significant and consistent improvements on relation extraction as compared with baselines. The source code of this paper can be obtained from https: //github.com/thunlp/PathNRE.