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Synergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN
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The SLAK framework synergizes LLM agents and a Location-Based Knowledge Graph (LBKG) for socioeconomic prediction. It leverages LLM agents' reasoning capabilities for automatic discovery and optimization of task-relevant meta-paths within the LBKG, and employs a semantic-guided knowledge fusion module for adaptive knowledge integration using LLM embeddings. Furthermore, it introduces a cross-task communication mechanism enabling both LLM agent-level collaboration for meta-path refinement and KG embedding-level fusion, positioning the LLMs as agents interacting with KGs for enhanced reasoning.
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