# Look Globally and Reason: Two-stage Path Reasoning over Sparse Knowledge Graphs

arXiv: https://arxiv.org/abs/2407.18556 · HTML: https://mlsanity.com/paper/2407.18556/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [TuckER: Tensor Factorization for Knowledge Graph Completion](https://mlsanity.com/paper/1901.09590/) (2019)
- [Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction](https://mlsanity.com/paper/2106.06935/) (2021)
- [InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions](https://mlsanity.com/paper/1911.00219/) (2019)
- [Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph](https://mlsanity.com/paper/2010.01899/) (2020)
- [Reinforced Anytime Bottom Up Rule Learning for Knowledge Graph Completion](https://mlsanity.com/paper/2004.04412/) (2020)
- [Collaborative Policy Learning for Open Knowledge Graph Reasoning](https://mlsanity.com/paper/1909.00230/) (2019)
- [KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion](https://mlsanity.com/paper/2208.07622/) (2022)
- [Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning](https://mlsanity.com/paper/2005.00571/) (2020)
- [SQUIRE: A Sequence-to-sequence Framework for Multi-hop Knowledge Graph Reasoning](https://mlsanity.com/paper/2201.06206/) (2022)
- [LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings](https://mlsanity.com/paper/2210.00305/) (2022)

## Built on by (most cited first, 2 of 2)

- [Understanding the Embedding Models on Hyper-relational Knowledge Graph](https://mlsanity.com/paper/2508.03280/) (2025)
- [StruProKGR: A Structural and Probabilistic Framework for Sparse Knowledge Graph Reasoning](https://mlsanity.com/paper/2512.12613/) (2025)

## Abstract (arXiv)

Sparse Knowledge Graphs (KGs), frequently encountered in real-world applications, contain fewer facts in the form of (head entity, relation, tail entity) compared to more populated KGs. The sparse KG completion task, which reasons answers for given queries in the form of (head entity, relation, ?) for sparse KGs, is particularly challenging due to the necessity of reasoning missing facts based on limited facts. Path-based models, known for excellent explainability, are often employed for this task. However, existing path-based models typically rely on external models to fill in missing facts and subsequently perform path reasoning. This approach introduces unexplainable factors or necessitates meticulous rule design. In light of this, this paper proposes an alternative approach by looking inward instead of seeking external assistance. We introduce a two-stage path reasoning model called LoGRe (Look Globally and Reason) over sparse KGs. LoGRe constructs a relation-path reasoning schema by globally analyzing the training data to alleviate the sparseness problem. Based on this schema, LoGRe then aggregates paths to reason out answers. Experimental results on five benchmark sparse KG datasets demonstrate the effectiveness of the proposed LoGRe model.