Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910883300507648 |
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| author | Zaoad, Md Shahir Zawad, Niamat Ranade, Priyanka Krogman, Richard Khan, Latifur Holt, James |
| author_facet | Zaoad, Md Shahir Zawad, Niamat Ranade, Priyanka Krogman, Richard Khan, Latifur Holt, James |
| contents | Knowledge graphs have emerged to be promising datastore candidates for context augmentation during Retrieval Augmented Generation (RAG). As a result, techniques in graph representation learning have been simultaneously explored alongside principal neural information retrieval approaches, such as two-phased retrieval, also known as re-ranking. While Graph Neural Networks (GNNs) have been proposed to demonstrate proficiency in graph learning for re-ranking, there are ongoing limitations in modeling and evaluating input graph structures for training and evaluation for passage and document ranking tasks. In this survey, we review emerging GNN-based ranking model architectures along with their corresponding graph representation construction methodologies. We conclude by providing recommendations on future research based on community-wide challenges and opportunities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14802 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities Zaoad, Md Shahir Zawad, Niamat Ranade, Priyanka Krogman, Richard Khan, Latifur Holt, James Information Retrieval N/A Knowledge graphs have emerged to be promising datastore candidates for context augmentation during Retrieval Augmented Generation (RAG). As a result, techniques in graph representation learning have been simultaneously explored alongside principal neural information retrieval approaches, such as two-phased retrieval, also known as re-ranking. While Graph Neural Networks (GNNs) have been proposed to demonstrate proficiency in graph learning for re-ranking, there are ongoing limitations in modeling and evaluating input graph structures for training and evaluation for passage and document ranking tasks. In this survey, we review emerging GNN-based ranking model architectures along with their corresponding graph representation construction methodologies. We conclude by providing recommendations on future research based on community-wide challenges and opportunities. |
| title | Graph-Based Re-ranking: Emerging Techniques, Limitations, and Opportunities |
| topic | Information Retrieval N/A |
| url | https://arxiv.org/abs/2503.14802 |