Using Obsidian as an Ontological Database for AI Agents
A new approach proposes transforming Obsidian from a simple data storage and search tool into an ontological database. This shift aims to help AI agents understand the complex relationships between different data points. As AI agents become more integrated into workflows, they need structured knowledge graphs rather than flat files to reason effectively. Using Obsidian as an ontology allows teams to build local, customizable knowledge bases that AI agents can navigate semantically. While traditional Obsidian usage focuses on storing and retrieving notes, an ontological approach defines formal categories, properties, and relationships. This structure enables AI agents to query and connect information programmatically, leveraging Obsidian's markdown-based local storage.
## BACKGROUND
Obsidian is a popular local-first markdown note-taking application that uses a graph view to link notes. An ontology in information science is a formal way of representing properties, relationships, and categories of concepts within a domain. Combining these allows users to turn a personal knowledge base into a machine-readable relational database.