梳理语义网发展脉络，融合机器学习与大模型新范式。
Semantic Web: Past, Present, and Future (with Machine Learning on Knowledge Graphs and Language Models on Knowledge Graphs)
- 从知识表示到推理链接，构建语义网核心框架
- 融合可信性、安全性和工业实践的现代升级
- 适合对知识图谱与大模型交叉应用感兴趣的读者
自提出以来，语义网激发了多代技术创新。语义技术被广泛用于在网页上共享海量信息，赋予其语义含义，并支持推断与推理。多年来，语义技术特别是知识图谱，在搜索引擎、数据集成、企业应用及机器学习中发挥了重要作用。本文回顾语义网的经典概念与基础，包括知识表示、网络知识的创建与验证、推理与链接、分布式查询；同时更新了近年来的新概念，如溯源、安全与信任，以及产业界贡献的实际影响。文章还综述了知识图谱的浅层与深层机器学习方法，探讨语言模型与知识图谱的关系，并展望语义网未来发展方向。
原文摘要 · Abstract (English)
Ever since the vision was formulated, the Semantic Web has inspired many generations of innovations. Semantic technologies have been used to share vast amounts of information on the Web, enhance them with semantics to give them meaning, and enable inference and reasoning on them. Throughout the years, semantic technologies, and in particular knowledge graphs, have been used in search engines, data integration, enterprise settings, and machine learning. In this paper, we recap the classical concepts and foundations of the Semantic Web as well as modern and recent concepts and applications, building upon these foundations. The classical topics we cover include knowledge representation, creating and validating knowledge on the Web, reasoning and linking, and distributed querying. We enhance this classical view of the so-called ``Semantic Web Layer Cake'' with an update of recent concepts. These include provenance, security and trust, as well as a discussion of practical impacts from industry-led contributions. We also provide an overiew of shallow and deep machine learning methods for knowledge graphs and discuss the relation of language models and knowledge graphs. We conclude with an outlook on the future directions of the Semantic Web.
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