SingularityNET Meeting Data Graph Database

Overview

A graph-based system for analyzing meeting relationships, participant interactions, and decision flows.

This utility imports SingularityNET meeting data into a Neo4j Aura graph database, creating a queryable graph structure of meetings, participants, documents, action items, and metadata.

Chek out our Wiki for detailed documentation and queries.

Data Model Visualization

Figure 1: Graph model showing relationships between Meetings and related entities

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Features

• Meeting Data Import: Converts JSON meeting data into a graph structure
• Graph Relationships: Creates connections between meetings, people, documents, and tasks
• Environment Variable Configuration: Securely loads Neo4j credentials from .env
• Error Handling: Comprehensive error logging for troubleshooting

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Prerequisites

Before diving into the analysis, ensure you have the following prerequisites installed and configured:

• Python 3.8 or later
• Neo4j Aura database instance
• Required Python packages:

  pip install neo4j python-dotenv

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Setup

1. Clone and Configure:

   git clone <repository-url>
   cd <repository-directory>

2. Create .env File:

   NEO4J_URI=neo4j+s://<your-database-uri>
   NEO4J_USERNAME=neo4j
   NEO4J_PASSWORD=<your-password>

3. Prepare Meeting Data: Place your meeting data in snet-data.json following this structure:

   {
       "meeting_id": [{
           "workgroup": "Governance Workgroup",
           "meetingInfo": {
               "name": "Weekly",
               "date": "2025-01-07",
               "host": "Host Name",
               "documenter": "Documenter Name",
               "peoplePresent": "Person1, Person2, Person3",
               "purpose": "Meeting Purpose",
               "workingDocs": [
                   {"title": "Doc Title", "link": "Doc URL"}
               ]
           },
           "agendaItems": [
               {
                   "status": "carry over",
                   "narrative": "Discussion narrative...",
                   "actionItems": [
                       {
                           "text": "Action Description",
                           "assignee": "Person Name",
                           "dueDate": "2025-01-14",
                           "status": "todo"
                       }
                   ],
                   "decisionItems": [
                       {
                           "decision": "Decision description",
                           "rationale": "Reasoning behind decision",
                           "opposing": "none",
                           "effect": "affectsOnlyThisWorkgroup"
                       }
                   ],
                   "discussionPoints": [
                       "Point 1",
                       "Point 2"
                   ]
               }
           ],
           "tags": {
               "topicsCovered": "Topic1, Topic2, Topic3",
               "emotions": "Productive, Collaborative"
           }
       }]
   }

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Usage

Run the importer:

python neo.py

Example Queries

View all relationships:

MATCH (n)-[r]->(m)
RETURN n, r, m

Show meetings and participants:

MATCH (p:Person)-[:ATTENDED]->(m:Meeting)
RETURN m.workgroup, m.date, collect(p.name) as participants

View action items and assignees:

MATCH (m:Meeting)-[:HAS_ACTION]->(a:ActionItem)-[:ASSIGNED_TO]->(p:Person)
RETURN m.date, a.text, p.name, a.status, a.dueDate

View meeting structure:

MATCH (p:Person)-[:ATTENDED]->(m:Meeting)
RETURN m.workgroup, m.date, collect(p.name) as participants

View meeting agenda items and decisions:

MATCH (m:Meeting)-[:HAS_AGENDA_ITEM]->(a:AgendaItem)-[:MADE_DECISION]->(d:Decision)
RETURN m.date, a.status, d.decision, d.rationale

View meeting metadata:

MATCH (m:Meeting)-[:COVERS_TOPIC]->(t:Topic)
RETURN m.date, m.workgroup, collect(t.name) as topics

MATCH (m:Meeting)-[:HAS_EMOTION]->(e:Emotion)
RETURN m.date, m.workgroup, collect(e.name) as emotions

MATCH (m:Meeting)
OPTIONAL MATCH (m)-[:COVERS_TOPIC]->(t:Topic)
OPTIONAL MATCH (m)-[:HAS_EMOTION]->(e:Emotion)
RETURN m.date, m.workgroup, 
       collect(DISTINCT t.name) as topics,
       collect(DISTINCT e.name) as emotions

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Files

• neo.py: Database connection and data import logic
• snet-data.json: Source meeting data
• .env: Neo4j credentials (not tracked in git)
• .gitignore: Git ignore rules

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Notes

• Ensure .env is in .gitignore to protect credentials
• Meeting data should follow the specified JSON structure
• Requires active Neo4j Aura instance

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License

MIT License

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