The **Semantic Web** is a project that intends to add computer-processable meaning ([[semantics]]) to the Word Wide Web. In Feb 2004, The World Wide Web Consortium (W3C) released the Resource Description Framework (RDF) and the _OWL Web Ontology Language_ (OWL). [[RDF]] is used to represent information and to exchange knowledge in the Web. [[OWL]] is used to publish and share sets of terms called [[ontology|ontologies]], supporting advanced Web search, software agents and [[knowledge management]]. The original promise of the Semantic Web hasn't been fully realized. These ideas were probably too idealistic. The cost of implementation was fairly high for organizations. Many of the tools were too technical or academic. Most importantly, unless the organization was focused on solving integration at scale (e.g., [[Wikidata]]), there was no real return on investment to the organization itself. These types of societal coordination problems are hard to solve in this top-down, decree-based way (even when it is a good idea). The emergence of the [[large language model|LLM]] has reignited interest in structured knowledge representations like [[Linked Data]] and the [[knowledge graph]]. In some ways, LLMs obviate the need for such structured representations--they excel at normalizing and disambiguating entities, relying on [[word embeddings]] to derive semantic meaning. However, knowledge graphs can act as a retrieval source for [[Retrieval Augmented Generation|RAG]], a reasoning substrate for logical consistency ([[entailment]]), and a domain constraint to generate factual answers from a [[knowledge base]]. The integration of semantic technologies with AI tools has become a topic of research interest. The frontier of this space now includes these applications in the fields of life sciences, healthcare, research, finance, security, and enterprise IT: - **Neuro-symbolic systems**: Combining LLMs with graphs for enhanced reasoning - **Graph RAG pipelines**: LLMs query a semantic knowledge graph instead of (or alongside) text chunks - **Auto-ontology generation**: LLMs help generate, map, or align ontologies for data - **Scientific and organizational KGs**: Still the best tools for modeling complex domains (e.g., molecules, diseases, corporate structure) ## Tim Berners-Lee