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Weifeng Jiang

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3 papers

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TMLR Journal 2025 Journal Article

Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs

Qi Hu
Weifeng Jiang
Haoran Li
Zihao Wang
Jiaxin Bai
Qianren Mao
Yangqiu Song
Lixin Fan

The increasing demand for deep learning-based foundation models has highlighted the importance of efficient data retrieval mechanisms. Neural graph databases (NGDBs) offer a compelling solution, leveraging neural spaces to store and query graph-structured data, thereby enabling LLMs to access precise and contextually relevant information. However, current NGDBs are constrained to single-graph operation, limiting their capacity to reason across multiple, distributed graphs. Furthermore, the lack of support for multi-source graph data in existing NGDBs hinders their ability to capture the complexity and diversity of real-world data. In many applications, data is distributed across multiple sources, and the ability to reason across these sources is crucial for making informed decisions. This limitation is particularly problematic when dealing with sensitive graph data, as directly sharing and aggregating such data poses significant privacy risks. As a result, many applications that rely on NGDBs are forced to choose between compromising data privacy or sacrificing the ability to reason across multiple graphs. To address these limitations, we propose to learn Federated Neural Graph DataBase (FedNGDB), a pioneering systematic framework that empowers privacy-preserving reasoning over multi-source graph data. FedNGDB leverages federated learning to collaboratively learn graph representations across multiple sources, enriching relationships between entities, and improving the overall quality of graph data. Unlike existing methods, FedNGDB can handle complex graph structures and relationships, making it suitable for various downstream tasks. We evaluate FedNGDBs on three real-world datasets, demonstrating its effectiveness in retrieving relevant information from multi-source graph data while keeping sensitive information secure on local devices. Our results show that FedNGDBs can efficiently retrieve answers to cross-graph queries, making it a promising approach for LLMs and other applications that rely on efficient data retrieval mechanisms.

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AAAI Conference 2024 Conference Paper

Is a Large Language Model a Good Annotator for Event Extraction?

Ruirui Chen
Chengwei Qin
Weifeng Jiang
Dongkyu Choi

Event extraction is an important task in natural language processing that focuses on mining event-related information from unstructured text. Despite considerable advancements, it is still challenging to achieve satisfactory performance in this task, and issues like data scarcity and imbalance obstruct progress. In this paper, we introduce an innovative approach where we employ Large Language Models (LLMs) as expert annotators for event extraction. We strategically include sample data from the training dataset in the prompt as a reference, ensuring alignment between the data distribution of LLM-generated samples and that of the benchmark dataset. This enables us to craft an augmented dataset that complements existing benchmarks, alleviating the challenges of data imbalance and scarcity and thereby enhancing the performance of fine-tuned models. We conducted extensive experiments to validate the efficacy of our proposed method, and we believe that this approach holds great potential for propelling the development and application of more advanced and reliable event extraction systems in real-world scenarios.

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ICML Conference 2024 Conference Paper

KnowFormer: Revisiting Transformers for Knowledge Graph Reasoning

Junnan Liu
Qianren Mao
Weifeng Jiang
Jianxin Li 0002

Knowledge graph reasoning plays a vital role in various applications and has garnered considerable attention. Recently, path-based methods have achieved impressive performance. However, they may face limitations stemming from constraints in message-passing neural networks, such as missing paths and information over-squashing. In this paper, we revisit the application of transformers for knowledge graph reasoning to address the constraints faced by path-based methods and propose a novel method KnowFormer. KnowFormer utilizes a transformer architecture to perform reasoning on knowledge graphs from the message-passing perspective, rather than reasoning by textual information like previous pretrained language model based methods. Specifically, we define the attention computation based on the query prototype of knowledge graph reasoning, facilitating convenient construction and efficient optimization. To incorporate structural information into the self-attention mechanism, we introduce structure-aware modules to calculate query, key, and value respectively. Additionally, we present an efficient attention computation method for better scalability. Experimental results demonstrate the superior performance of KnowFormer compared to prominent baseline methods on both transductive and inductive benchmarks.

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