Microsoft Academic Graph Information Retrieval for Research Recommendation and Assistance
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914211685203968 |
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| author | Shiwakoti, Shikshya Goldsmith, Samuel Pandit, Ujjwal |
| author_facet | Shiwakoti, Shikshya Goldsmith, Samuel Pandit, Ujjwal |
| contents | In today's information-driven world, access to scientific publications has become increasingly easy. At the same time, filtering through the massive volume of available research has become more challenging than ever. Graph Neural Networks (GNNs) and graph attention mechanisms have shown strong effectiveness in searching large-scale information databases, particularly when combined with modern large language models. In this paper, we propose an Attention-Based Subgraph Retriever, a GNN-as-retriever model that applies attention-based pruning to extract a refined subgraph, which is then passed to a large language model for advanced knowledge reasoning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16661 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Microsoft Academic Graph Information Retrieval for Research Recommendation and Assistance Shiwakoti, Shikshya Goldsmith, Samuel Pandit, Ujjwal Information Retrieval Artificial Intelligence In today's information-driven world, access to scientific publications has become increasingly easy. At the same time, filtering through the massive volume of available research has become more challenging than ever. Graph Neural Networks (GNNs) and graph attention mechanisms have shown strong effectiveness in searching large-scale information databases, particularly when combined with modern large language models. In this paper, we propose an Attention-Based Subgraph Retriever, a GNN-as-retriever model that applies attention-based pruning to extract a refined subgraph, which is then passed to a large language model for advanced knowledge reasoning. |
| title | Microsoft Academic Graph Information Retrieval for Research Recommendation and Assistance |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2512.16661 |