语义阶梯：一种用于知识图谱和人工智能系统自然语言内容逐步形式化的框架
The Semantic Ladder: A Framework for Progressive Formalization of Natural Language Content for Knowledge Graphs and AI Systems
AI总结：
本文提出语义阶梯框架，通过逐步形式化自然语言内容，解决自然语言与形式化模型之间的表示差异问题，支持知识图谱和AI系统的可扩展性和互操作性。
摘要：
语义数据和知识基础设施必须弥合两种根本不同的表示形式：自然语言，其中大多数知识被创建和交流，以及形式语义模型，这些模型使机器可操作的集成、互操作性和推理成为可能。弥合这一差距仍然是一个核心挑战，尤其是在数据输入点需要完全语义形式化时。本文介绍了语义阶梯，一种架构框架，能够实现数据和知识的逐步形式化。基于模块化语义单元的概念，该框架在语义明确性逐渐增加的层次上组织表示，从自然语言文本片段到基于本体和更高阶逻辑模型。不同层次之间的转换支持语义丰富、陈述结构化和逻辑建模，同时保持语义连续性和可追溯性。这种方法使语义知识空间的逐步构建成为可能，减轻了语义解析的负担，并支持异构表示的集成，包括自然语言、结构化语义模型和向量嵌入。因此，语义阶梯为可扩展、互操作且AI友好的数据和知识基础设施提供了基础。
英文摘要：
Semantic data and knowledge infrastructures must reconcile two fundamentally different forms of representation: natural language, in which most knowledge is created and communicated, and formal semantic models, which enable machine-actionable integration, interoperability, and reasoning. Bridging this gap remains a central challenge, particularly when full semantic formalization is required at the point of data entry. Here, we introduce the Semantic Ladder, an architectural framework that enables the progressive formalization of data and knowledge. Building on the concept of modular semantic units as identifiable carriers of meaning, the framework organizes representations across levels of increasing semantic explicitness, ranging from natural language text snippets to ontology-based and higher-order logical models. Transformations between levels support semantic enrichment, statement structuring, and logical modelling while preserving semantic continuity and traceability. This approach enables the incremental construction of semantic knowledge spaces, reduces the semantic parsing burden, and supports the integration of heterogeneous representations, including natural language, structured semantic models, and vector-based embeddings. The Semantic Ladder thereby provides a foundation for scalable, interoperable, and AI-ready data and knowledge infrastructures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
