利用大语言模型智能体与知识图谱的协同作用进行城市社会经济预测
Harnessing the Synergy between LLM Agents and Knowledge Graphs for Urban Socioeconomic Prediction
AI总结：
本研究提出LLM智能体与知识图谱的协同框架，结合二者优势完成城市社会经济预测，经实验验证该协同设计可提升预测效果，为跨任务信息共享提供思路。
摘要：
社会经济预测旨在利用各类城市数据预测区域的人口、商业活动水平等社会经济指标，这对理解城市区域和支持决策具有重要作用。现有研究利用知识图谱（KG）对异构城市数据进行建模，并进一步将图表示学习方法应用于社会经济预测。然而，这些方法严重依赖启发式思路和专业知识从多样数据中提取任务相关知识，可能对特定任务并非最优；此外，它们往往忽略不同指标之间的固有关系，限制了预测精度。受大语言模型（LLM）卓越能力的启发，本研究提出一种LLM智能体与KG的协同框架，将KG上的推理与表示学习和LLM智能体相整合。我们首先构建城市知识图谱（UrbanKG）以建模多源城市数据，并微调嵌入语言模型以生成带有语义信息的KG实体嵌入。接着，我们利用LLM的推理能力为各类社会经济预测任务识别UrbanKG中相关的元路径，并设计语义引导注意力模块以融合元路径知识。此外，我们引入跨任务通信机制，通过在LLM智能体和KG层面实现跨任务知识共享来进一步提升性能：一方面，不同任务的LLM智能体协作生成更多样、更全面的元路径；另一方面，不同任务的嵌入被自适应融合。在两个数据集上的实验证明了LLM与KG协同设计的有效性，为社会经济预测任务间的信息共享提供了见解。
英文摘要：
Socioeconomic prediction aims to leverage various urban data to predict the socioeconomic indicators of regions such as population and commercial activity level, which plays an important role in understanding urban regions and supporting decision-making. Existing studies leverage knowledge graphs (KG) to model heterogeneous urban data, and further apply graph representation learning methods for socioeconomic prediction. However, these approaches heavily rely on heuristic ideas and expertise to extract task-relevant knowledge from diverse data, which may not be optimal for specific tasks. Additionally, they tend to overlook the inherent relationships between different indicators, limiting the prediction accuracy. Motivated by the remarkable abilities of large language models (LLMs), in this work, we propose a synergistic framework of LLM agents and KG, which integrates the reasoning and representation learning on KG with LLM agents. We first construct an urban knowledge graph (UrbanKG) to model multi-sourced urban data and finetune an embedding language model to generate embeddings for KG entities with semantic information. Then we leverage the reasoning power of LLM to identify relevant meta-paths in the UrbanKG for each type of socioeconomic prediction task, and design a semantic-guided attention module for knowledge fusion with meta-paths. Moreover, we introduce a cross-task communication mechanism to further enhance performance by enabling knowledge sharing across tasks at both LLM agent and KG levels. On the one hand, the LLM agents for different tasks collaborate to generate more diverse and comprehensive meta-paths. On the other hand, the embeddings from different tasks are adaptively merged. Experiments on two datasets demonstrate the effectiveness of the synergistic design between LLM and KG, providing insights for information sharing across socioeconomic prediction tasks.
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