用本体论统一管理AI模型与数据资产，解决工业场景下的语义鸿沟问题。
Ontology-supported AI Model and Dataset Management
- 基于本体论构建AI资产管理体系，实现模型与数据的标准化描述。
- 平台支持模型与数据的交换、分析与追踪，提升可复用性。
- 在实时关键系统中验证有效，适合工业AI研发团队使用。
近年来，针对改进AI方法及其应用的研究日益增多，核心目标是追踪进展、实现透明比较，并深化对AI的理解。在此过程中，各组织生成并使用大量需追踪、溯源和管理的资产，且发现相关资产对当前任务至关重要。本文研究旨在回答在工业场景下如何无语义差异地有效交换与管理AI模型及相关资产。我们提出一个AI模型交换平台，支持模型与数据的使用、交换与分析。该平台整合本体论，促进对任务需求的深层共识，解决上述挑战。最后，通过真实实时关键系统中的用例展示了平台的实用性。
原文摘要 · Abstract (English)
Recently, there has been a great deal of research into improving AI methods and their application. The main focus is on tracking progress, enabling transparent comparisons, and fostering a more profound understanding of AI. In that process, different organizations generate and use plenty of assets that need to be tracked, traced and managed. Moreover, it is important to discover assets relevant for the task at hand. This paper presents research aiming to contribute to answering the question of what is required to exchange and manage AI models and related assets effectively without semantic gaps in an industrial context. We introduce a platform for AI model exchange, which facilitates the usage, exchange, and analysis of AI models and datasets. The platform incorporates an ontology that can foster a more profound common understanding of what is required in these tasks and help tackle the issues mentioned above. Finally, we elucidate the utility of the platform through the illustration of a use case in the context of real-time critical systems.
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