Enterprise AI Analysis
Microsoft Academic Graph Information Retrieval for Research Recommendation and Assistance
This paper proposes an Attention Based Subgraph Retriever, a GNN-as-retriever model that uses attention pruning to pass a refined subgraph to an LLM for advanced knowledge reasoning. It addresses the challenge of filtering scientific publications in large corpora by leveraging Graph Neural Networks (GNNs) and attention mechanisms for information retrieval and citation recommendation, further enhanced by LLM-based reranking. The model was evaluated on a subset of the Microsoft Academic Graph (MAG) dataset.
Key Executive Impact
Our analysis reveals the following key impacts for your enterprise:
0.30%
Average Precision@10
0.23%
LLM Reranking Precision@10
1500
Papers Processed (Evaluation)
Deep Analysis & Enterprise Applications
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Introduction
Background
Approach
Results
Discussion & Challenges
Limitations & Future Work
Introduction
Explores the growing challenge of information retrieval in vast research corpora and introduces the concept of GraphRAG and GNNs for enhanced retrieval.
Background
Details the prior work on GNNs for information retrieval, focusing on models like GNN-RAG, and highlights the importance of attention and pooling mechanisms in GNNs like SAGPool.
Approach
Outlines the proposed Attention Based Subgraph Retriever model, its GNN-as-retriever architecture, the dataset used (Microsoft Academic Graph), pre-processing steps, and the integration of LLM-based reranking.
Results
Presents the evaluation metrics (Precision@k, Recall@k, MRR, nDCG@k) and compares the model's performance against baselines like BM25, SBERT, and Hybrid approaches, including the impact of LLM-based reranking.
Discussion & Challenges
Discusses the difficulties encountered, such as data pre-processing, incompatibility of SAGPool with heterogeneous data, and the pivot to homogeneous graphs.
Limitations & Future Work
Acknowledges the limitations of using homogeneous data and an outdated, sparse dataset, while suggesting future directions including the use of heterogeneous graphs and dynamic knowledge graphs.
0.24%
Initial Model Recall@10
Enterprise Process Flow
Query to Seed Node
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GNN Retrieval (Attention Scores)
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Subgraph Pruning (SAGPool)
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Subgraph Expansion
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LLM Reranking
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Citation Recommendation
| Feature | Attention Based Subgraph Retriever | Hybrid BM25+SBERT Baseline |
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Case Study: GNN-RAG for Question Answering
Mavromatis and Karypis (2024) demonstrated the efficacy of GNN-RAG in leveraging knowledge graphs for question-answering. Their GNN reasoned over dense subgraphs to provide candidate answers to LLMs, showcasing the power of GNNs in complex information retrieval tasks.
Impact: Significant improvement in retrieving relevant information by using GNNs to reason over graph structures, providing richer context than semantic search alone for LLMs.
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Estimated Annual Savings
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