Inside CORE-KG: Evaluating Structured Prompting and Coreference Resolution for Knowledge Graphs
NeutralArtificial Intelligence
The article discusses the challenges of analyzing human smuggling networks through legal case documents, which are often unstructured and complex. It highlights the limitations of current automated knowledge graph construction methods, particularly those based on large language models (LLMs), which tend to produce fragmented and noisy outputs. This research is significant as it seeks to improve the accuracy and reliability of knowledge graphs, which are essential for understanding and combating human smuggling.
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