为机器学习模型建立可机器读取的元数据标准，支持知识图谱集成。
MRM3: Machine Readable ML Model Metadata
- 设计结构化元数据格式，支持机器解析与知识图谱整合。
- 构建包含22个模型、113个节点和199条关系的无线定位模型知识图谱。
- 适用于需要模型可追溯性与环境影响评估的研发团队。
随着机器学习模型的数量和复杂度持续增长，良好的模型文档对开发者和企业至关重要。目前在Hugging Face等平台中以模型卡片形式存在的元数据多为非结构化信息，难以被机器直接读取。本文提出一种结构化元数据规范，支持机器可读，并可集成至知识图谱（KG）以实现更高效组织与查询，拓展应用场景。同时，我们构建了一个包含22个无线定位模型、基于4个数据集训练的元数据数据集，并将其整合进基于Neo4j的知识图谱，共形成113个节点与199条关系。该方案还支持能源消耗与碳足迹等环境影响指标的纳入。
原文摘要 · Abstract (English)
As the complexity and number of machine learning (ML) models grows, well-documented ML models are essential for developers and companies to use or adapt them to their specific use cases. Model metadata, already present in unstructured format as model cards in online repositories such as Hugging Face, could be more structured and machine readable while also incorporating environmental impact metrics such as energy consumption and carbon footprint. Our work extends the existing State of the Art by defining a structured schema for ML model metadata focusing on machine-readable format and support for integration into a knowledge graph (KG) for better organization and querying, enabling a wider set of use cases. Furthermore, we present an example wireless localization model metadata dataset consisting of 22 models trained on 4 datasets, integrated into a Neo4j-based KG with 113 nodes and 199 relations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。