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LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities

License: MIT License

Python 100.00%
chatgpt event-extraction gpt4 information-extraction knowledge-graph knowledge-graph-completion knowledge-graph-construction link-prediction relation-extraction relational-triple-extraction large-language-models

autokg's Issues

知识图谱构建效果

运行了autokg代码 如果我有一个文档(包含很多段文字) 构建对应知识图谱 (不需要外部信息)如何实现? 我运行的结果是几段对话 而并不是三元组

How to regenerate results for link prediction on FB15K-237?

Hi there,

Amazing work! Was wondering if you can provide some code files or jupyter notebooks to regenerate the results for the link prediction task on FB15K-237? I feed your provided prompts into chatgpt but couldn't regenerate your results given the output it provides.

Thank you,

Ryan

Evaluation Process for Event Extraction from MAVEN Dataset

Hello,

I have a question regarding your evaluation process for Event extraction from the MAVEN dataset. I was unable to locate any code detailing your approach.

For instance, in your one-shot prompt:

Given a sentence: "Unprepared for the attack, the Swedish attempted to save their ships by cutting their anchor ropes and to flee."
Event types: Removing, Rescuing, Escaping, Attack, Self-motion

You are seeking event types as listed above. My question pertains to how you calculate the F1 score for a LLM when some data samples contain one event per sentence while others contain multiple events within a sentence. Could you please elucidate how you calculate precision and recall for each sample and overall for a model?

Thank you.

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