An exploration of the role of an ontologist, covering skills, tasks, differences from taxonomists, training resources, and the future of the field.
The article explores how modern AI agents are fulfilling the vision of the Semantic Web by combining AI's learned intuition with the logical structure of semantic technologies, creating intelligent agents that can understand and act on behalf of users.
This article details a step-by-step guide on building a knowledge graph from plain text using an LLM-powered pipeline. It covers concepts like Subject-Predicate-Object triples, text chunking, and LLM prompting to extract structured information.
The article explores how Retrieval-Augmented Generation (RAG) and knowledge graphs can be used together to break down data silos and enable more accurate, context-aware, and insightful AI systems.
Ontop is a Virtual Knowledge Graph system that exposes the content of arbitrary relational databases as knowledge graphs using SPARQL. It translates SPARQL queries into SQL queries and relies on R2RML mappings and can utilize lightweight ontologies.
"...a feature that activates when Claude reads a scam email (this presumably supports the model’s ability to recognize such emails and warn you not to respond to them). Normally, if one asks Claude to generate a scam email, it will refuse to do so. But when we ask the same question with the feature artificially activated sufficiently strongly, this overcomes Claude's harmlessness training and it responds by drafting a scam email."
python ./ontology_viz.py -o test.dot test.ttl -O ontology.ttl
