用大模型自动提取实验数据中的关系并标注，助力实验室数据互通。
RELRaE: LLM-Based Relationship Extraction, Labelling, Refinement, and Evaluation
- 分阶段使用大模型提取XML中隐含的关系并生成标签
- 验证了大模型在关系标注任务中具备高准确率
- 适合参与实验室自动化与本体构建的研究者
机器人在实验室实验中产生大量XML数据。为支持实验室间的数据互操作性，需将这些数据转换为知识图谱，其中关键步骤是丰富XML模式以构建本体模式。本文提出RELRaE框架，利用大语言模型在不同阶段提取并准确标注XML模式中隐含的关系。我们研究了大模型生成关系标签的准确性，并进行了评估。结果表明，大模型可有效支持实验室自动化场景下的关系标签生成，并在半自动本体构建框架中具有重要价值。
原文摘要 · Abstract (English)
A large volume of XML data is produced in experiments carried out by robots in laboratories. In order to support the interoperability of data between labs, there is a motivation to translate the XML data into a knowledge graph. A key stage of this process is the enrichment of the XML schema to lay the foundation of an ontology schema. To achieve this, we present the RELRaE framework, a framework that employs large language models in different stages to extract and accurately label the relationships implicitly present in the XML schema. We investigate the capability of LLMs to accurately generate these labels and then evaluate them. Our work demonstrates that LLMs can be effectively used to support the generation of relationship labels in the context of lab automation, and that they can play a valuable role within semi-automatic ontology generation frameworks more generally.
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