From the semantic web to the synthetic web: Knowledge graphs as infrastructure for generative AI
DOI:
https://doi.org/10.3145/thinkepi.2025.e19a30Keywords:
Knowledge graphs, Generative AI, LLMs, Semantic web, Synthetic web, WikidataAbstract
The semantic web was born with the idea of publishing structured and interconnected data. Its adoption was limited by technical barriers, a lack of accessible tools and a weak culture of open data. Even so, projects like DBpedia and, above all, Wikidata demonstrated that knowledge graphs can support reliable and useful applications in specific domains. The emergence of generative AI based on LLMs has changed the landscape. These systems produce fluent responses, but also hallucinations and a lack of traceability. In addition, their knowledge is implicit and difficult to update. This has increased interest in integrating generative AI with knowledge graphs to provide precision, coherence and verification. For this reason, hybrid approaches combining LLMs with knowledge graphs are emerging: RAG techniques that query RDF graphs, triple generation, assistance in knowledge engineering, graph-constrained reasoning and training with structured data. In this context, Wikidata has become a key infrastructure for a “synthetic web”, where its capacity to represent structured data and its potential for factual verification make it possible to build more reliable generative AI systems.Downloads
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