用提示链让大模型自动理解结构化数据语义
Knowledge prompt chaining for semantic modeling
- 将图结构知识转为提示链注入大模型
- 仅用少量输入数据就超越现有方法
- 适合需要自动标注结构数据的场景
构建如CSV、JSON、XML等结构化数据的语义表示在知识表示领域至关重要。尽管互联网上存在海量结构化数据，但将其映射到领域本体以建立语义仍具挑战性，因需模型理解图结构知识，否则需人工投入。本文提出一种新型自动语义建模框架：Knowledge Prompt Chaining。该框架可将图结构知识序列化，并通过提示链架构合理注入大模型。借助知识注入与提示链机制，模型能自然学习图的结构信息与潜在空间，生成符合链指令的语义标签与语义图。实验表明，该方法在使用更少结构化输入数据的情况下，性能优于现有领先技术。
原文摘要 · Abstract (English)
The task of building semantics for structured data such as CSV, JSON, and XML files is highly relevant in the knowledge representation field. Even though we have a vast of structured data on the internet, mapping them to domain ontologies to build semantics for them is still very challenging as it requires the construction model to understand and learn graph-structured knowledge. Otherwise, the task will require human beings' effort and cost. In this paper, we proposed a novel automatic semantic modeling framework: Knowledge Prompt Chaining. It can serialize the graph-structured knowledge and inject it into the LLMs properly in a Prompt Chaining architecture. Through this knowledge injection and prompting chaining, the model in our framework can learn the structure information and latent space of the graph and generate the semantic labels and semantic graphs following the chains' insturction naturally. Based on experimental results, our method achieves better performance than existing leading techniques, despite using reduced structured input data.
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