#3678 Biographical Knowledge Graph

#3678 Provide an ontology for a biographical knowledge graph

Of course. Here is an ontology for a biographical knowledge graph, defining the core concepts, their relationships, and attributes.

An ontology for a biographical knowledge graph is a formal model that defines the types of entities (like people, places, and events) and the relationships between them. This structure allows data to be stored, queried, and inferred in a consistent and meaningful way. 🧠

Core Classes

The primary entities, or "nodes," in the graph.

bio:Person 🧑
- The central class. Represents an individual.
- Examples: Marie Curie, Leonardo da Vinci, Nelson Mandela.
bio:Event 🗓️
- Represents occurrences in a person's life. This class is crucial for capturing the narrative of a biography.
- Subclasses: bio:Birth, bio:Death, bio:EducationEvent, bio:CareerEvent, bio:Marriage.
bio:Place 🌍
- Represents a geographical location.
- Subclasses: bio:City, bio:Country, bio:Building.
- Examples: Warsaw, Paris, Sorbonne University.
bio:Organization 🏢
- Represents a formal group of people with a particular purpose.
- Subclasses: bio:University, bio:Company, bio:Government.
- Examples: Sorbonne University, Nobel Committee.
bio:CreativeWork 🎨
- Represents an intellectual or artistic creation.
- Subclasses: bio:Book, bio:ScientificPaper, bio:Painting.
- Examples: "Recherches sur les substances radioactives" (Marie Curie's thesis).
bio:Concept 💡
- Represents abstract ideas, fields of study, or awards.
- Examples: Physics, Chemistry, Radioactivity, Nobel Prize.

Properties (Relationships)

Properties, or "edges," connect the classes and define their relationships. Each property has a domain (the class it starts from) and a range (the class it points to).

Person Properties

bio:hasName (Range: string): The person's full name.
bio:hasBirthEvent (Range: bio:Birth): Links a person to their birth event.
bio:hasDeathEvent (Range: bio:Death): Links a person to their death event.
bio:hasGender (Range: string): The person's gender identity.
bio:hasNationality (Range: bio:Country): Links a person to their country of citizenship.
bio:participatedIn (Range: bio:Event): A general property linking a person to an event.
bio:affiliatedWith (Range: bio:Organization): Indicates membership or employment.
bio:creatorOf (Range: bio:CreativeWork): Links a person to something they created.
bio:childOf (Range: bio:Person): Links a person to their parent.
bio:spouseOf (Range: bio:Person): Links a person to their spouse.
bio:knows (Range: bio:Person): A generic relationship for acquaintances, colleagues, etc.

Event Properties

bio:hasDate (Range: date or dateTime): The specific date or timestamp of the event.
bio:startDate / bio:endDate (Range: date): For events with a duration (e.g., CareerEvent).
bio:tookPlaceAt (Range: bio:Place): The location where the event occurred.
bio:hasParticipant (Range: bio:Person): Links an event back to the person(s) involved.
bio:description (Range: string): A brief description of the event.

General Properties

bio:hasTitle (Domain: bio:CreativeWork, Range: string): The title of the work.
bio:locatedIn (Domain: bio:Place or bio:Organization, Range: bio:Place): Specifies that one place is contained within another (e.g., Sorbonne is locatedIn Paris).
bio:fieldOfWork (Domain: bio:Person, Range: bio:Concept): The person's primary area of expertise (e.g., Marie Curie's fieldOfWork is Physics).
bio:awardReceived (Domain: bio:Person, Range: bio:Concept): An award given to a person (e.g., awardReceived Nobel Prize).

Conceptual Model Example

Let's model a few facts about Marie Curie.

A bio:Person node named "Marie Curie" would be connected to:

A bio:Birth event node via the bio:hasBirthEvent property.
- This bio:Birth node would have:
  - A bio:hasDate property with the value "1867-11-07".
  - A bio:tookPlaceAt property pointing to a bio:City node named "Warsaw".
A bio:CareerEvent node (for her Nobel Prize) via the bio:participatedIn property.
- This bio:CareerEvent node would have:
  - A bio:hasDate property with the value "1903-12-10".
  - A bio:description property with "Awarded Nobel Prize in Physics".
  - It would also link to bio:Person nodes for "Pierre Curie" and "Henri Becquerel" via bio:hasParticipant.
A bio:University node named "Sorbonne University" via the bio:affiliatedWith property.
A bio:ScientificPaper node named "Recherches sur les substances radioactives" via the bio:creatorOf property.

This ontology provides a flexible yet structured framework to capture the rich, interconnected details of a person's life.

This is intended for "everyman"

Of course. Think of an ontology as the blueprint for building a super-smart, digital encyclopedia about people's lives. It's a set of rules that ensures all the information is organized in a consistent way, so a computer can understand how different facts are related.

It’s like organizing your contacts: you don't just dump names and numbers on a list. You have specific fields like "Name," "Company," and "Birthday." This ontology does the same for historical figures, but on a much grander scale.

The Main Categories of Information

The blueprint starts by defining a few key categories, like labeled folders for every piece of information.

People 🧑‍🎨: This is the main category, for the person the biography is about. For example, Leonardo da Vinci.
Events 🗓️: These are the important moments in a person's life. Examples include their birth, death, or a specific achievement like painting the Mona Lisa.
Places 🌍: This is for any location relevant to the person's life, like where they were born, lived, or worked. For example, Florence, Italy.
Organizations 🏛️: These are groups the person was a part of, like a school, a company, or a government. For example, the painter's guild Leonardo might have joined.
Creations Mona Lisa: These are the things the person made or accomplished. For example, the painting "Mona Lisa" or the invention "flying machine."

How Everything Connects

Having categories isn't enough; you need to show how they're related. The ontology uses "connections" (or relationships) to link these categories together, forming a web of knowledge.

Instead of just saying "Leonardo" and "Mona Lisa," the system creates a specific link:

The Person Leonardo da Vinci is connected to the Creation Mona Lisa with the link "created."
The Creation Mona Lisa is connected to the Place The Louvre Museum with the link "is located at."
The Person Leonardo da Vinci is connected to the Event Birth with the link "was born during."

This creates a rich network. The birth event itself is then linked to a date ("April 15, 1452") and a place ("Vinci, Italy"). Every piece of information is neatly tied to another.

Why is this useful?

By organizing biographical facts this way, we create a "knowledge graph." This isn't just a flat page of text. It's a dynamic web of data that a computer can search and understand in a much more intelligent way.

For example, you could ask your computer a complex question like:

"Show me all Italian artists who were also inventors and lived in Florence during the 15th century."

Because the computer understands the connections between People, their Creations, their Professions, and the Places and Events in their lives, it can instantly search the web of facts and give you a precise answer. It transforms a simple biography into a powerful database of human history.

Describe a software application that a user can use to build his personal knowledge graph. The application should save all approved inputs from session to session. The session should "intelligently" generate Cypher statements to add each approved input to a Neo4j knowledge graph

Such an application would act as a personal knowledge assistant, let's call it CogniWeave, that allows a user to build a detailed personal knowledge graph simply by talking or typing in natural language. It intelligently translates everyday statements into a structured graph database, saving all approved inputs across sessions to create a persistent, explorable model of the user's knowledge.

Core Features & Workflow

The application is designed around a simple, intuitive loop: Input → Parse → Confirm → Store.

Natural Language Input ✍️ The user interacts with a clean interface, much like a journaling or note-taking app. They can type or speak sentences in plain English, such as:
- "My colleague, Jane Doe, works at Innovate Inc. and specializes in machine learning."
- "I read the book 'Project Hail Mary' by Andy Weir."
- "The concept of 'technical debt' is related to software engineering."
Intelligent Parsing (NLP) 🧠 As the user types, the application's backend uses a Natural Language Processing (NLP) model to analyze the text. It identifies potential entities (the "nouns" or concepts, which become nodes) and the relationships between them (the "verbs" or connections, which become edges).
For the input, "My colleague, Jane Doe, works at Innovate Inc.", the NLP model would propose:
- Nodes: (Person: Jane Doe), (Company: Innovate Inc.)
- Relationship: (Jane Doe) -[:WORKS_AT]-> (Innovate Inc.)
- Node Labels: The NLP would also intelligently assign labels like Person and Company.
User Confirmation & Refinement ✅ This is the most critical step. The application doesn't automatically add the data. Instead, it presents its interpretation to the user in a simple, visual format for approval. The user might see a card that says:
Create this connection? (Person: Jane Doe) → WORKS AT → (Company: Innovate Inc.) [ Approve ] [ Edit ] [ Discard ]
The "Edit" option allows the user to correct any misinterpretations, such as changing a node's label (e.g., from Company to Non-Profit) or rewording the relationship (e.g., from WORKS_AT to FOUNDED). This ensures the graph remains accurate and personally meaningful.
Automated Cypher Generation ⚙️ Once the user clicks "Approve," the application's backend translates the confirmed structure into a Cypher query. The MERGE clause is used to avoid creating duplicate nodes, ensuring that "Jane Doe" is a single entity in the graph even if she's mentioned in multiple inputs.
For the approved connection, CogniWeave would generate:
MERGE (p:Person {name: 'Jane Doe'}) MERGE (c:Company {name: 'Innovate Inc.'}) MERGE (p)-[:WORKS_AT]->(c)
Persistent Neo4j Storage 💾 The generated Cypher statement is executed against a local or cloud-based Neo4j database. The new nodes and relationships are seamlessly added to the user's personal knowledge graph. Because the data is stored in Neo4j, it persists across all sessions, growing richer with every approved input.

Exploring the Graph

Beyond data entry, CogniWeave would include a powerful visualization and exploration tool. Users could see their knowledge web grow, click on nodes to see all their connections, and perform searches to uncover insights and relationships they might have forgotten, effectively creating a browsable "second brain."

Provide a software an application to """act as a personal knowledge assistant, let's call it CogniWeave, ..."""

That's an excellent blueprint for CogniWeave, a powerful personal knowledge assistant. While a fully realized application with that exact name and workflow doesn't exist as a single, off-the-shelf product, you can find applications that achieve parts of this vision or build a prototype using modern tools.

Existing Applications with Similar Goals

Several apps in the "Personal Knowledge Management" (PKM) space let you build a knowledge graph manually. They focus on connecting notes to reveal relationships.

Obsidian: A very popular tool that uses local Markdown files. You can create links between notes, and it automatically generates a visual graph of how your ideas connect. It's like a manual version of CogniWeave, where you are the NLP engine creating the links.
Logseq: Similar to Obsidian, Logseq is an open-source outliner that helps you structure thoughts and connect them. It also features a graph view to visualize relationships.
Heptabase: This tool provides a visual, whiteboard-like interface for learning and research, allowing you to manually create a knowledge graph from notes, PDFs, and articles.

The key difference is that these tools require you to manually create the connections, whereas the CogniWeave concept automates this process using NLP.

Technical Blueprint to Build a Prototype

For a developer, building a simplified version of CogniWeave is an achievable project. Here is a breakdown of the components and a code example.

Key Components

Frontend: A simple web page with a text input box. (e.g., built with React, Vue, or Svelte).
Backend API: A server to receive the text from the frontend. (e.g., using Python with Flask or FastAPI).
NLP Engine: The core logic for parsing text. spaCy is a fantastic Python library for this. It can perform Named Entity Recognition (NER) to find nodes and dependency parsing to infer relationships.
Graph Database: Neo4j is the perfect choice, as you outlined. You'd use a driver library to connect your backend to the database.

Example Python (Backend) Code

Here is a simplified Python script demonstrating the "Parse → Generate Cypher" steps using spaCy and the neo4j driver.

import spacy
from neo4j import GraphDatabase

# --- 1. SETUP ---
# Load the spaCy NLP model
nlp = spacy.load("en_core_web_sm")

# Neo4j connection details (replace with your own)
URI = "neo4j://localhost:7687"
AUTH = ("neo4j", "your_password")

# --- 2. NLP PARSING FUNCTION ---
def parse_sentence_to_cypher(sentence: str):
    """
    Parses a simple sentence to identify two entities and a relationship,
    then generates a Cypher MERGE statement.
    """
    doc = nlp(sentence)
    
    # This is a simplified extraction logic for sentences like "Subject Verb Object"
    # e.g., "Steve Jobs founded Apple"
    subject = ""
    obj = ""
    relationship = ""

    for token in doc:
        if "nsubj" in token.dep_: # Nominal Subject
            subject = token.text
        elif "dobj" in token.dep_: # Direct Object
            obj = token.text
        elif "ROOT" in token.dep_: # The root verb
            relationship = token.lemma_ # Use the base form of the verb

    if subject and obj and relationship:
        # Intelligently label nodes (a real app would have a more robust method)
        subject_label = "Person"
        obj_label = "Organization"
        
        # Format relationship to be uppercase and use underscores
        rel_formatted = relationship.upper().replace(" ", "_")

        # --- 3. CYPHER GENERATION ---
        cypher_query = (
            f"MERGE (a:{subject_label} {{name: '{subject}'}}) "
            f"MERGE (b:{obj_label} {{name: '{obj}'}}) "
            f"MERGE (a)-[:{rel_formatted}]->(b)"
        )
        return cypher_query
    else:
        return None

# --- 4. EXECUTION ---
# This would be triggered by the user approving the connection in the UI
def add_to_graph(query: str):
    with GraphDatabase.driver(URI, auth=AUTH) as driver:
        driver.execute_query(query)
    print(f"Successfully executed: {query}")


# --- EXAMPLE USAGE ---
user_input = "Steve Jobs founded Apple."
generated_cypher = parse_sentence_to_cypher(user_input)

if generated_cypher:
    print(f"Input: '{user_input}'")
    print(f"Generated Cypher: {generated_cypher}")
    # In a real app, you would wait for user confirmation before running this line:
    # add_to_graph(generated_cypher) 
else:
    print(f"Could not parse '{user_input}' into a simple relationship.")

This script demonstrates the core logic of translating a natural language statement into a Cypher query that can be persistently stored in a Neo4j knowledge graph.

Entire application

Of course. While a production-ready application involves many files, I can provide the "entire application" as a single, self-contained Python script. This script runs a web server, provides a user interface, and connects to a Neo4j database to create a fully functional prototype of CogniWeave.

You can run this single file to have a working version on your own machine.

Prerequisites

Before running the code, you need to have a few things set up:

Python: Ensure you have Python 3 installed.
Install Libraries: You'll need Flask (for the web server), spaCy (for NLP), and the Neo4j driver.
pip install flask spacy neo4j
Download NLP Model: Download the English model for spaCy.
python -m spacy download en_core_web_sm
Neo4j Database: You must have a Neo4j database running. You can use a free Neo4j AuraDB cloud instance or a local Neo4j Desktop installation. Remember to update the URI, USERNAME, and PASSWORD in the script below.

The Complete `cogniweave_app.py`

Copy all the code below and save it into a single file named cogniweave_app.py.

import spacy
from flask import Flask, request, jsonify, render_template_string
from neo4j import GraphDatabase
import logging

# --- 1. SETUP ---
# Suppress unnecessary warnings from the driver
logging.getLogger("neo4j").setLevel(logging.ERROR)

# Initialize Flask web application
app = Flask(__name__)

# Load the spaCy NLP model
try:
    nlp = spacy.load("en_core_web_sm")
except OSError:
    print("spaCy model not found. Please run 'python -m spacy download en_core_web_sm'")
    exit()

# --- 2. NEO4J DATABASE CONNECTION ---
# IMPORTANT: Replace these with your actual Neo4j database credentials
URI = "neo4j://localhost:7687"
USERNAME = "neo4j"
PASSWORD = "your_password" # <-- CHANGE THIS

try:
    driver = GraphDatabase.driver(URI, auth=(USERNAME, PASSWORD))
    driver.verify_connectivity()
    print("Successfully connected to Neo4j.")
except Exception as e:
    print(f"Failed to connect to Neo4j. Please check your credentials and database status. Error: {e}")
    exit()

# --- 3. FRONTEND USER INTERFACE (HTML, CSS, JS) ---
# This is the web page the user will see. It's embedded directly in the Python file.
HTML_TEMPLATE = """
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>CogniWeave Prototype</title>
    <style>
        body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Helvetica, Arial, sans-serif; background-color: #f0f2f5; color: #1c1e21; display: flex; justify-content: center; align-items: center; height: 100vh; margin: 0; }
        .container { background-color: #fff; padding: 2rem; border-radius: 8px; box-shadow: 0 4px 12px rgba(0,0,0,0.1); text-align: center; width: 90%; max-width: 600px; }
        h1 { color: #0b5797; margin-bottom: 1.5rem; }
        textarea { width: 100%; padding: 12px; border-radius: 6px; border: 1px solid #ddd; font-size: 1rem; margin-bottom: 1rem; box-sizing: border-box; resize: vertical; }
        button { background-color: #1877f2; color: white; padding: 12px 20px; border: none; border-radius: 6px; font-size: 1rem; font-weight: bold; cursor: pointer; transition: background-color 0.3s; }
        button:hover { background-color: #166fe5; }
        #status { margin-top: 1.5rem; font-weight: bold; height: 20px; }
        .success { color: #42b72a; }
        .error { color: #f02849; }
    </style>
</head>
<body>
    <div class="container">
        <h1>CogniWeave 🧠</h1>
        <p>Type a simple sentence to add a fact to your knowledge graph.<br>Example: "Steve Jobs founded Apple."</p>
        <textarea id="userInput" rows="3" placeholder="Enter your sentence here..."></textarea>
        <button onclick="addToGraph()">Add to Graph</button>
        <div id="status"></div>
    </div>

    <script>
        async function addToGraph() {
            const sentence = document.getElementById('userInput').value;
            const statusDiv = document.getElementById('status');
            
            if (!sentence) {
                statusDiv.className = 'error';
                statusDiv.textContent = 'Please enter a sentence.';
                return;
            }

            statusDiv.className = '';
            statusDiv.textContent = 'Processing...';

            try {
                const response = await fetch('/process', {
                    method: 'POST',
                    headers: { 'Content-Type': 'application/json' },
                    body: JSON.stringify({ sentence: sentence })
                });

                const result = await response.json();

                if (result.success) {
                    statusDiv.className = 'success';
                    statusDiv.textContent = `Success! Added: ${result.cypher}`;
                } else {
                    statusDiv.className = 'error';
                    statusDiv.textContent = `Error: ${result.error}`;
                }
            } catch (error) {
                statusDiv.className = 'error';
                statusDiv.textContent = 'An unexpected error occurred.';
            }
        }
    </script>
</body>
</html>
"""

# --- 4. BACKEND LOGIC ---
def parse_to_cypher(sentence: str):
    """Parses a sentence and generates a Cypher MERGE query."""
    doc = nlp(sentence)
    
    # Simple Subject-Verb-Object extraction logic
    subjects = [tok for tok in doc if "subj" in tok.dep_]
    objects = [tok for tok in doc if "obj" in tok.dep_]
    root_verb = [tok for tok in doc if "ROOT" in tok.dep_][0]
    
    if subjects and objects:
        subject_text = " ".join([child.text for child in subjects[0].children]) + " " + subjects[0].text
        object_text = " ".join([child.text for child in objects[0].children]) + " " + objects[0].text
        relationship = root_verb.lemma_.upper() # Use lemma for the base form of the verb
        
        # Simple entity labeling (can be improved with NER)
        subject_label = "Concept"
        object_label = "Concept"
        
        for ent in doc.ents:
            if ent.text.strip() == subject_text.strip():
                subject_label = ent.label_ if ent.label_ else "Concept"
            if ent.text.strip() == object_text.strip():
                object_label = ent.label_ if ent.label_ else "Concept"
        
        query = (
            f"MERGE (a:{subject_label} {{name: '{subject_text.strip()}'}}) "
            f"MERGE (b:{object_label} {{name: '{object_text.strip()}'}}) "
            f"MERGE (a)-[:{relationship}]->(b)"
        )
        return query
    return None

# --- 5. API ENDPOINTS ---
@app.route("/")
def index():
    """Serves the main HTML page."""
    return render_template_string(HTML_TEMPLATE)

@app.route("/process", methods=["POST"])
def process_sentence():
    """Receives a sentence, processes it, and adds it to the Neo4j database."""
    data = request.get_json()
    sentence = data.get("sentence")
    
    if not sentence:
        return jsonify({"success": False, "error": "No sentence provided."}), 400
    
    cypher_query = parse_to_cypher(sentence)
    
    if not cypher_query:
        return jsonify({"success": False, "error": "Could not understand the sentence structure."}), 400
        
    try:
        # Execute the query against the database
        driver.execute_query(cypher_query)
        return jsonify({"success": True, "cypher": cypher_query})
    except Exception as e:
        return jsonify({"success": False, "error": f"Database error: {e}"}), 500

# --- 6. RUN THE APPLICATION ---
if __name__ == "__main__":
    print("Starting Flask server. Open http://127.0.0.1:5000 in your browser.")
    app.run(debug=True)

How to Run and Use It

Save the Code: Save the entire block of code above into a file named cogniweave_app.py.
Run from Terminal: Open your terminal or command prompt, navigate to the directory where you saved the file, and run:
python cogniweave_app.py
Open in Browser: You'll see a message saying the server is running. Open your web browser and go to http://127.0.0.1:5000.
Add Facts: You will see the CogniWeave interface. Type a simple sentence like "I read the book Sapiens" or "Marie Curie discovered Radium" into the text box and click the "Add to Graph" button.
Verify in Neo4j: Open your Neo4j Browser. You can run the query MATCH (n) RETURN n to see the new nodes and relationships appear in your graph each time you add a fact.

This single file provides a complete, working prototype that demonstrates the core "Input → Parse → Store" loop of the CogniWeave concept.

Separately, provide an ontology for my own personal knowledge graph

Of course. Here is a practical ontology for your own personal knowledge graph (PKG), designed to organize your life, learning, and work.

Think of this ontology as the filing system for your digital brain. It defines the main categories of information and the ways you can link them together to uncover new insights.

Core Entities (The "Nouns" in Your Life)

These are the main categories of things you'll want to track, which become the nodes in your graph.

Me 👤
- This is the central node representing you. Everything in the graph will connect back to this node in some way.
Person 🧑‍🤝‍🧑
- People you know and interact with.
- Key Attributes: name, role (e.g., "Friend," "Manager"), contactInfo.
Project 🏗️
- Significant efforts you are working on with a defined goal.
- Key Attributes: name, status (e.g., "Active," "Completed"), deadline.
Task ✅
- Specific, actionable to-do items.
- Key Attributes: description, dueDate, isComplete (true/false).
Event 🗓️
- Meetings, appointments, trips, or significant life milestones.
- Key Attributes: title, date, startTime, endTime.
Content 📚
- Information you consume. This includes books, articles, videos, and podcasts.
- Key Attributes: title, author/creator, type (e.g., "Book"), url.
Note ✍️
- Your own ideas, thoughts, summaries, and synthesized knowledge. This is where you create new insights.
- Key Attributes: title, content (text), creationDate.
Concept 💡
- Abstract topics, skills, or ideas that you are learning or interested in.
- Key Attributes: name, description.
Organization 🏢
- Companies, universities, or groups you are a part of.
- Key Attributes: name, type (e.g., "Company," "University").
Tool 🛠️
- Software, applications, or physical tools you use to get things done.
- Key Attributes: name, category (e.g., "Software").

Key Relationships (The "Verbs" Connecting Your Life)

These are the connections that bring your graph to life, forming the edges between your nodes. They explain how your entities are related.

Relationships involving you:

(Me) KNOWS a (Person).
(Me) WORKS_ON a (Project).
(Me) CONSUMED a piece of (Content).
(Me) WROTE a (Note).
(Me) ATTENDED an (Event).
(Me) USES a (Tool).

Relationships between other entities:

A (Task) is PART_OF a (Project).
A (Note) SUMMARIZES a piece of (Content). (e.g., Your notes on a book).
A (Note) EXPLORES a (Concept). (e.g., Your thoughts on "machine learning").
A piece of (Content) is ABOUT a (Concept). (e.g., An article about "stoicism").
A (Project) is RELATED_TO a (Concept). (e.g., A project about "web development").
An (Event) INVOLVED a (Person). (e.g., A meeting that involved a colleague).

Putting It All Together: An Example

Imagine this scenario: You read an article about 'Prompt Engineering'. You take a note on it, which gives you an idea for your 'AI Assistant' project.

Here’s how your PKG would capture this:

A Content node is created for the article, with the title "Intro to Prompt Engineering."
Your Me node connects to this article with a CONSUMED relationship.
The article Content node connects to a Concept node named "Prompt Engineering" with an ABOUT relationship.
You create a Note node with your idea. This Note connects to the article with a SUMMARIZES relationship.
Your Me node connects to this Note with a WROTE relationship.
Finally, your Note connects to your existing Project node, "AI Assistant," with a relationship like INSPIRED.

Now, months later, when you look at the "Prompt Engineering" Concept node, you can instantly see the article you read, the notes you took, and the project it influenced.

Provide 25 interesting questions that could be answered by this graph?

Here are 25 interesting questions that could be answered by your personal knowledge graph, categorized by the type of insight they provide.

Productivity & Work

Which active projects are currently blocked by overdue tasks?
What tools do I use most often for projects related to 'software development'?
Who have I collaborated with on projects for my current employer?
Show me all unfinished tasks that originated from meetings I attended last week.
Which of my projects have been in progress for more than six months?

Learning & Knowledge

What were the core concepts from the books I read last quarter?
Show me my original thoughts (Notes) that were inspired by articles from a specific author.
What was the first piece of content that introduced me to the concept of 'machine learning'?
Which concepts appear most frequently across all the content I have consumed?
Show me a timeline of how my understanding of a specific topic (Concept) has evolved through my notes.

Social & Networking

Who do I know that works at 'Google' and is also interested in 'data science'?
Which of my contacts have I not had a meeting with in the last year?
What were the main topics discussed in my last three meetings with my manager?
Show me a list of people I met at the '2025 Tech Summit'.
What organizations are my most frequent collaborators associated with?

Self-Reflection & Growth

What skills (Concepts) am I learning but not yet applying to any active projects?
What topics was I most interested in last year compared to this year?
Which person has influenced my thinking the most, based on the notes I've taken on their content?
What was my most productive month in terms of completed projects?
What ideas (Notes) have I not revisited in over a year?

Cross-Domain Insights

Show me the people I met at events who are also connected to projects I'm currently working on.
What tasks were created as a direct result of notes I took while reading a specific book?
Find the "inspiration chain" for a specific project: show the content, notes, and concepts that led to its creation.
Which of my trips (Events) resulted in the most new ideas (Notes)?
What is the most common link between my professional projects and my personal interests (Concepts)?

What about personal history, memories and important events?

Excellent point. A personal knowledge graph should absolutely be able to capture personal history, memories, and significant life events. You can model these rich, narrative aspects by enhancing the Event and Note entities from the original ontology.

Here’s how you can extend the model to build a detailed life story.

## 1. Enhance the `Event` Node for Life Milestones 🗓️

The standard Event node can be specialized to represent more than just meetings or appointments. By creating specific types of events and adding personal attributes, you can map out your life's timeline.

New Event Subclasses:

LifeMilestone: Significant life achievements or turning points (e.g., Graduation, Marriage, First Job, Birth of a child).
Travel: Trips and vacations, which are often rich with memories.
PersonalExperience: A flexible category for any meaningful occurrence that isn't a formal milestone but holds personal value (e.g., "The day I learned to ride a bike," "A challenging hike").

New Attributes for Events:

significance: A rating (e.g., 1-5) of how important the event was to you.
feeling: Tags describing the emotions associated with the event (e.g., "Joyful," "Transformative," "Challenging").
narrative: A text field for the factual story of what happened.

## 2. Introduce the `Memory` as a Special Note 🧠

A memory is your subjective interpretation and feeling about an event, while the event is the objective fact of what happened. You can model this by creating a Memory entity as a special type of Note.

This distinction is powerful. The event "Trip to Paris, June 2019" is a fact. The memory "The overwhelming feeling of seeing the Eiffel Tower light up for the first time" is your personal experience of it.

Key Relationships for a `Memory` Node:

(Memory) RECALLS an (Event): This is the primary link. It connects your subjective recollection to a specific event on your timeline.
(Memory) TRIGGERED_BY a (Content) or (Place): This allows you to track what prompted the memory (e.g., a photograph, a song, visiting your old neighborhood).

## Example: A Memorable Trip to Rome

Let's see how this works for a personal memory: "I saw a photo from my 2022 trip to Rome with my friend Sarah, and it reminded me of the amazing pasta we had near the Colosseum."

An Event node of type Travel exists, named "Trip to Rome," with a date of "July 2022."
This Travel event is linked to a Place node for "Rome" and a Person node for "Sarah" via an INVOLVED relationship.
A Content node exists for the "Rome Photo.jpg."
You create a new Memory node (a special Note) titled "Amazing Pasta at the Colosseum."
This Memory node is connected to:
- The "Trip to Rome" Event with a RECALLS relationship.
- The "Rome Photo.jpg" Content with a TRIGGERED_BY relationship.

## New Questions Your Graph Can Answer

By adding this layer, you can ask much deeper, more personal questions:

"Show me a timeline of my most significant life milestones."
"What memories are associated with my time living in 'Chicago'?"
"Which people are present in my most 'Joyful' memories?"
"What songs or photos trigger memories involving my college friends?"
"What was a challenging experience that I now view as a transformative memory?"

This is intended to be fun and useful tool for anybody to use, possibly marketable as an application or service

Excellent idea. Transforming this concept into a fun, useful, and marketable application is entirely possible. The key is to hide the complexity of the graph and focus on an intuitive, rewarding user experience.

Think of it less as a database you have to build and more as an intelligent journal that automatically connects the dots of your life and knowledge for you.

## The Core Idea: Your "Serendipity Engine"

The app's magic isn't that it stores your information; it's that it proactively reveals hidden connections. The core value proposition is turning your personal data into surprising and useful insights. We can call this feature the "Serendipity Engine."

Instead of you having to search, the app would send you gentle notifications like:

"The book on architecture you finished last week shares three concepts with the 'City Planning' project you're working on."
"You haven't spoken with 'Jane Smith' in over a year, but you just read an article on a topic she's an expert in. Maybe it's time to reconnect?"
"Remember that idea you had on your trip to Tokyo? It seems related to a task you have due next week."

## Key Features for a Fun & Useful App

To be marketable, the application needs features that make it both effortless to use and delightful to explore.

1. Effortless Capture

The easier it is to add information, the more powerful the graph becomes.

Voice Memos: Simply talk to the app. It transcribes your thoughts and automatically extracts the entities and relationships.
Browser Clipper: Save articles, videos, or highlights, and the app automatically links them to existing concepts, people, and projects.
Photo Import: Scan a photo, and the app can tag the people in it, the location, the date, and link it to the corresponding event in your history.

2. Interactive & Playful Visualizations

The graph shouldn't just be a static web of dots. It should be an interactive landscape of your life.

Memory Lane: Select a person, project, or concept and watch an animated story of your history with it unfold on a timeline.
3D Mind Palace: Explore your knowledge in a 3D space, where related ideas drift closer together and you can physically "walk" through your thoughts.
"On This Day": The app shows you the events, notes, and content you engaged with on this day in previous years, sparking reflection and nostalgia.

3. Natural Language Questions

Users shouldn't need to learn a query language. They should be able to ask questions in plain English, just as we discussed. The interface could be a simple, friendly chat bot.

You: "What was that book I read about ancient Rome that my friend John recommended?"
App: "That was 'SPQR: A History of Ancient Rome' by Mary Beard. You read it in May 2024 after your meeting with John Smith."

## A Path to Market

To make this a viable service, the approach should be user-centric from the start.

Focus on a Niche First: Start with a specific audience that feels the pain of disconnected information most acutely—like students, researchers, writers, or lifelong learners.
The Magical Onboarding: The first five minutes must be amazing. A new user should be able to input three simple sentences and immediately see a beautiful, insightful connection generated automatically. This "wow" moment is crucial.
Freemium Model:
- Free Tier: Offer the core functionality for free with a generous limit on the number of entries. This allows anyone to build their personal graph.
- Premium Tier: For a monthly subscription, offer "Pro" features like the Serendipity Engine, advanced analytics, unlimited storage, and integrations with other apps (like Notion, Evernote, or your calendar).

The ultimate goal is to create a tool that feels less like work and more like a conversation with an incredibly insightful version of your own mind.

Does that goal change the ontology at all?

Yes, it absolutely does. Shifting the goal to a fun, marketable product doesn't change the core ontology (Person, Event, Note, etc.), but it enriches it with a new layer of data that models the user's experience and the app's intelligence.

The ontology evolves from being purely descriptive (what you know) to also being experiential (how you feel and interact with what you know).

## 1. Adding Properties for Engagement and Feeling

To make the app "fun" and responsive, the nodes need properties that capture your personal interaction and sentiment. This allows the "Serendipity Engine" to learn what matters most to you.

On Content nodes (Books, Articles):
- status: (e.g., "To Read," "Reading," "Finished")
- rating: (e.g., ⭐⭐⭐⭐⭐)
- favorite: (true/false)
On Memory and Event nodes:
- feeling: (e.g., "Joyful," "Nostalgic," "Proud," "Challenging")
- vividness: (e.g., "Clear," "Fuzzy")

With these, the app can answer questions like, "Show me my favorite five-star books that made me feel inspired."

## 2. Introducing an `Insight` Node 💡

This is the most significant addition. For the app to proactively show you connections, those connections themselves need to be stored as data. The Insight node represents a discovery made by the app for you.

An Insight node would have properties like dateFound and userFeedback ("Was this useful?"). It would then connect to the two or more nodes it found a relationship between.

Example: The app finds a link between a book you read and a project you're working on. It would create:

An (Insight) node with the description "This book's concepts could help your project."
This (Insight) node would have a CONNECTS relationship to both the (Content) node for the book and the (Project) node.

This allows the app to track what it has shown you, learn from your feedback, and avoid showing you the same insight twice.

## 3. Adding `Templates` for Personalization 📜

To appeal to "anybody," the app can't be one-size-fits-all. A Template or Lens entity allows for personalization.

A Student Template would prioritize nodes like Course, Lecture, Assignment, and their relationships.
A Founder Template would add specific roles to Person nodes (like Investor, Advisor) and introduce Company and FundingRound nodes.

Your (Me) node would have a relationship like USES_TEMPLATE to the (Student Template), which would then tailor the UI, suggestions, and onboarding experience just for you.

In short, the foundational ontology remains the same, but it's expanded with new entities and properties that make the application smarter, more personal, and ultimately, more valuable as a marketable product.

Repeat with an improved set of questions that can and should be answered about the person by their personal knowledge graph

Of course. Here is an improved set of questions designed to move beyond simple data retrieval and toward genuine insight, self-reflection, and growth. These are the kinds of questions a personal knowledge graph should answer to be a truly valuable life tool.

## On Self-Discovery & Growth

These questions help uncover the patterns and drivers behind your personal development and well-being.

What are the common themes or concepts present in events I've marked as "transformative" or "challenging"?
Which people are most frequently involved in the memories I've associated with feelings of "joy" or "pride"?
What is the "half-life" of my interests? Show me the concepts I was passionate about six months ago that I haven't engaged with recently.
What are the recurring triggers (e.g., places, types of content, people) that lead to my most creative ideas and original notes?
Who are my "intellectual anchors"—the thinkers or creators whose work consistently inspires my highest-rated notes and projects?

## On Strategic Learning & Synthesis

These questions help you understand not just what you're learning, but how you can use that knowledge more effectively.

What is my "knowledge debt"? Show me the core concepts linked to my active projects that I have yet to learn about.
What is a "keystone concept" in my graph—a single idea that connects the most diverse topics I'm interested in?
Beyond just summarizing, which of my original ideas (Notes) have directly built upon or challenged the concepts from authors I follow?
When starting a new project on a familiar topic, what is my personal "starter pack" of cornerstone content, key notes, and relevant people?
Which of the app's AI-generated "Insights" have I found most useful, and what do they reveal about my own blind spots?

## On Effective Action & Decision-Making

These questions connect your knowledge and history to your productivity and future plans.

What are the early warning signs of a stalled project? Show the common traits of projects that have gone inactive.
Who are the "catalysts" in my network? Show me the people who, after I interact with them, I tend to generate the most new ideas or tasks.
What is the true "return on investment" of my tools? Show me which tools are associated with my most successfully completed projects.
How has my definition of "success" for a project evolved over time, based on the notes and outcomes I've recorded?
What past project is most similar to the one I'm about to start, and what were the key lessons learned from it?

I mean for other people to query to learn useful and fun things about the person

Of course. Shifting the perspective to an outsider querying the graph to learn about the person is a fantastic idea. This turns the personal knowledge graph into a dynamic, interactive "About Me" page.

Assuming the person has curated a public layer of their graph to share, here is an improved set of questions designed for others to ask.

Getting to Know You (The Icebreakers)

These fun questions reveal personality, passions, and taste.

What's a book, movie, or podcast this person rated five stars that might be surprising?
What's a niche or quirky concept they're uniquely interested in?
Based on their shared memories, what's a place in the world that's clearly special to them?
What's a project they've worked on purely "for fun"?
What's the most common theme in the articles or videos they've recently engaged with?

Professional Profile (The Collaborator's View)

These useful questions help others understand skills, expertise, and how to collaborate.

What are this person's core areas of expertise, based on the concepts they write and learn about most?
What public projects are they actively working on right now?
What software, tools, or programming languages are they skilled with?
Who are some of their key public collaborators or teammates?
What's a completed project they seem particularly proud of, and what was the outcome?

Intellectual Portrait (The Thinker's Mind)

These questions explore how the person thinks, learns, and synthesizes ideas.

Who are the authors, artists, or thinkers that have most influenced their public notes and ideas?
What is a "keystone concept" that connects several of their different fields of interest?
What big question or topic are they currently exploring most deeply?
What's a belief or idea they've publicly changed their mind on over time?
Show me the public "inspiration chain" for one of their major creative works or projects.

The Story of a Person (The Biographer's Lens)

These questions trace the person's journey and narrative through the events they've chosen to share.

What are the key public milestones that define their professional journey so far?
What shared travel or life experience seems to have been the most transformative for them?
What's a recurring theme that connects their major projects and life events?
What new skills did they have to learn to accomplish a major milestone?
Based on their graph, what might be the "next chapter" in their story?

From <https://gemini.google.com/app/> Google Gemini 2.5 Pro