Building Research Ontologies in Capacities App: Complete Guide

Quick Answer: Building research ontologies in Capacities app involves moving away from hierarchical folders to an object-based system. By defining specific object types (like Books, Concepts, and Authors) and establishing relationships between them using properties and tags, you create a structured, interconnected database that surfaces insights and mirrors natural human thought processes.

Academic and professional research often hits a wall when the volume of notes outgrows traditional folder-based systems. You start a project with clear intentions, but months later, finding a specific argument or connecting disparate concepts across hundreds of text files feels impossible. The cognitive load of remembering where you put something overrides the actual work of synthesizing ideas.

This is the primary failure of rigid, hierarchical knowledge management. When ideas are forced into single containers, their relationships to other concepts are severed.

Capacities approaches this problem fundamentally differently by utilizing an object-based architecture. Instead of asking “Where does this note live?”, Capacities asks “What is this piece of information?” By shifting to this paradigm, researchers can construct sophisticated ontologies—structured networks of concepts and their relationships—that evolve naturally alongside their understanding.

This guide details exactly how to approach building research ontologies in Capacities app, moving from basic object creation to advanced query networks for complex knowledge work.

Understanding Object-Based Note-Taking

To build an effective research ontology, you must first understand the core mechanic of Capacities: objects.

In traditional note-taking apps (like Evernote or basic Markdown editors), everything is a generic “note.” In an object-based system, information is categorized by its fundamental nature. A meeting is not the same as a book, and a psychological concept is not the same as a person.

The Shift from Folders to Types

When building an ontology, your first task is defining your taxonomy. Instead of creating a folder named “Philosophy Research,” you define an object type called Concept and another called Philosopher.

This seemingly small shift has massive implications. By classifying information by its type, you can assign specific properties to each category. A Book object can have properties for “Author,” “Publication Year,” and “Reading Status,” while a Meeting object has properties for “Date,” “Attendees,” and “Action Items.”

Properties as Relational Connectors

Properties are the connective tissue of your ontology. While backlinks (wiki-links) are useful for inline context, properties allow for structured data relationships. If every Book object requires you to link an Author object, you are inherently building a graph of relationships. Over time, navigating to an Author will automatically display every Book you’ve logged by them, without any manual curation required.

Designing Your Core Research Ontology

A successful ontology balances structure with flexibility. Over-engineering your object types initially will create friction, while under-engineering will result in a messy, unsearchable database.

Essential Object Types for Researchers

Most research workflows can be managed effectively with four to six core object types:

1. Source / Reference: The origin of the information (Books, Articles, Podcasts, Videos).
2. Person / Creator: The author, speaker, or subject of the source.
3. Concept / Topic: The abstract ideas, theories, or frameworks you are studying.
4. Claim / Argument: Specific assertions made within the sources.
5. Project / Output: The final deliverable you are working toward (Essay, Dissertation, Video Script).

Defining Properties for Maximum Retrieval

Once your core types are established, defining the right properties ensures your ontology is queryable.

For a Source object, essential properties include:

• Type: (Select field: Book, Journal Article, Webpage)
• Author: (Link to Person object)
• Date Published: (Date field)
• Status: (Select field: To Read, Reading, Processed)
• Concepts Discussed: (Multi-link to Concept objects)

For a Concept object, keep properties streamlined:

• Parent Concept: (Link to another Concept object for hierarchical relationships)
• Related Concepts: (Multi-link to other Concept objects)
• Key Sources: (Multi-link to Source objects)

By strictly applying these properties as you input data, you guarantee that every new node in your network is properly connected to the existing web of knowledge.

Managing the Input Pipeline

An ontology is only as useful as the data within it. The friction of adding new information often determines whether a system succeeds or fails. Capacities provides specific tools to streamline the ingestion of research material.

Handling Daily Notes and Fleeting Thoughts

Not every piece of information belongs in your formal ontology immediately. Capacities uses a Daily Note structure as a staging ground.

When you encounter an interesting quote or have a sudden realization, log it on the Daily Note without worrying about its final destination. During a weekly review, you can transform these generic blocks into specific objects (e.g., turning a bullet point into a Concept object) and link them appropriately. This separates the act of capturing from the act of structuring.

Web Clipping and External Integrations

For robust research, manual data entry is unsustainable. Use the Capacities web clipper or mobile integration (via Telegram or WhatsApp) to send articles, tweets, and images directly into your workspace.

When configuring the web clipper, set default behaviors to categorize incoming web pages as a specific object type (like a Web Source) so they bypass the inbox and directly enter your ontological structure, ready for processing.

Visualizing and Querying the Knowledge Graph

The primary benefit of building research ontologies in Capacities app is the ability to view your information from multiple angles. Since data isn’t trapped in folders, you can use queries and views to surface insights dynamically.

Utilizing the Graph View

Capacities generates a visual graph of your knowledge base. While a global graph can quickly become overwhelming, local graphs are invaluable.

When viewing a specific Concept object, the local graph reveals not only the notes you explicitly linked but also second-degree connections. You might see that a specific Author frequently appears adjacent to this concept, prompting a new line of inquiry you hadn’t considered.

Creating Dynamic Dashboards with Queries

Instead of manually curating lists of relevant notes, use Capacities’ querying features to create self-updating dashboards.

You can create a wall or table view that filters for:

• Object Type: Source
• Status: Processed
• Tag: #cognitive_psychology

As you continue your research and mark new sources as processed, this dashboard automatically updates. This dynamic retrieval means you spend less time organizing and more time synthesizing.

Practical Advice for Maintaining the System

Even the best-designed ontology requires maintenance. Without regular pruning and consistent habits, the database will degrade into a digital junk drawer.

Start Small and Iterate

Do not try to map your entire research domain on day one. Start with three object types (e.g., Source, Person, Concept) and use them for a week. Notice where you experience friction. Are you trying to force a methodology into a Concept object when it really needs its own Framework object type? Adjust your schema based on actual usage, not theoretical perfection.

The Two-Minute Linking Rule

When creating a new object, spend a maximum of two minutes linking it to existing objects. If you read a new article on machine learning, immediately link it to the author, the core concepts discussed, and any related projects. If you cannot find the relevant links within two minutes, create the object anyway and rely on full-text search later. The goal is adequate connection, not exhaustive indexing.

Distinguish Between Tags and Objects

A common mistake when building research ontologies is confusing tags with objects.

Use Objects for entities that have their own properties and notes. “Artificial Intelligence” should be a Concept object because you will want to write notes about it, define it, and link sources to it.

Use Tags for status, context, or workflow states. “#review_later”, “#high_priority”, or “#needs_citation” are tags. They describe the state of the information, not the information itself. Mixing these up leads to cluttered graphs and confusing property fields.

Conclusion

Building research ontologies in Capacities app requires a fundamental shift from storing files to mapping relationships. By carefully defining object types and their properties, you create a system that reflects the interconnected nature of human knowledge. Start with a lean taxonomy, rigorously link new inputs, and leverage dynamic queries to surface insights. When executed correctly, Capacities transforms from a simple digital filing cabinet into an active partner in your research process, illuminating connections that would otherwise remain hidden.

Frequently Asked Questions

What is the difference between a folder and an object in Capacities?

A folder is a location where a file is stored, limiting it to one context. An object is a predefined type of information (like a Book or Meeting) that can exist in multiple contexts simultaneously through relationships and properties, regardless of where it was created.

Can I import my existing Markdown notes into Capacities?

Yes, Capacities supports Markdown imports. However, because imported notes come in as generic text pages, you will need to manually convert them into specific object types (like Concepts or Sources) to fully utilize the ontological features of the app.

How many object types should I create for academic research?

Aim for 4 to 6 core object types initially: Sources, Authors, Concepts, Claims, and Projects. Keeping the number low reduces cognitive load when entering new information and prevents your database schema from becoming overly complex and difficult to query.

Is Capacities better than Obsidian for building ontologies?

It depends on your technical preference. Capacities provides a structured, out-of-the-box object database that is easier to set up for relational mapping. Obsidian offers more flexibility and local file ownership but requires significant configuration with community plugins (like Dataview) to achieve the same database-like functionality.

Do I need to fill out every property when creating a new object?

No. Properties should serve as prompts, not mandatory forms. Fill out the properties that are immediately useful for future retrieval (like Author or Date) and leave the rest blank. You can always enrich the metadata later as the object becomes more central to your research.

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