Note-Taking Application Using Markdown, Word Embeddings, and k-NN for Similarity Organization

1. Introduction

1.1 Concept

A note-taking application that uses Markdown for note formatting and leverages Doc2Vec for document embeddings and k-nearest neighbors (k-NN) to organize notes into a tree structure based on their similarities.

The research Juputer notebook with promising results

1.3 Definitions

• Markdown: A lightweight markup language with plain-text formatting syntax.
• Document Embedding: A type of document representation that allows documents to be represented as vectors in a continuous vector space. (See Word Embedding)
• k-Nearest Neighbors (k-NN): A machine learning algorithm used to find the k most similar items in a dataset.
• TF-IDF: Term Frequency-Inverse Document Frequency, a statistical measure used to evaluate the importance of a word in a document relative to a collection of documents.

2. Requirements

2.1 Functional Requirements

• Note Creation: Users must be able to create and edit notes using Markdown.
• Note Storage: Notes must be stored persistently in a database.
• Note Embedding: The application must convert notes into vector representations using document embeddings.
• Similarity Calculation: The application must calculate similarities between notes using k-NN.
• Tree Structure: The application must organize notes into a tree structure based on their similarities.
• Visualization: The application must provide a visual representation of the similarity tree.

2.2 Non-Functional Requirements

• Performance: The application should perform similarity calculations and tree updates efficiently.
• Scalability: The application should handle a large number of notes without significant performance degradation.
• Usability: The application should be user-friendly and intuitive.
• Security: Notes should be stored securely and access-controlled.

3. System Architecture

3.1 Overview

The system architecture consists of the following components:

• Frontend: A web-based user interface for note creation, editing, and visualization.
• Backend: A server-side application responsible for handling requests, processing notes, and managing the database.
• Database: A persistent storage system for notes and embeddings.

4. Data Flow

1. Note Creation: User creates/edits a note in the Markdown editor.
2. Note Submission: The note is submitted to the backend via the API.
3. Embedding Generation: The embedding service generates a vector representation of the note.
4. Note Storage: The note and its embedding are stored in the database.
5. Similarity Calculation: The similarity service calculates similarities between the new note and existing notes using k-NN.
6. Tree Update: The tree builder updates the similarity tree based on the new note's embedding.
7. Visualization Update: The frontend updates the visualization to reflect the updated tree.

5. Implementation Details

5.1 Preprocessing

• Tokenization: Split notes into words.
• Cleaning: Remove punctuation, lowercase, and stop words.

5.2 Embedding Generation

• Model Selection: Doc2Vec.
• Vector Representation: Generate vectors for each note and aggregate them (e.g., averaging).

5.3 TF-IDF Weighting

• TF-IDF Calculation: Compute TF-IDF weights for words in notes.
• Weighted Embedding: Multiply word vectors by their TF-IDF weights and aggregate to get note embeddings.

5.4 Similarity Calculation

• k-NN Algorithm: Use k-NN to find the N most similar notes based on vector distance (e.g., cosine similarity).

5.5 Tree Construction

• Hierarchical Clustering: Use a clustering algorithm to group similar notes and create a tree structure.
• Tree Update: Dynamically update the tree as new notes are added.

6. API Endpoints

6.1 Note Management

• POST /notes: Create a new note.
• GET /notes/{id}: Retrieve a specific note.
• PUT /notes/{id}: Update a specific note.
• DELETE /notes/{id}: Delete a specific note.

7. Security Considerations

• Authentication: Implement user authentication to secure access to notes.
• Authorization: Implement role-based access control to manage permissions.
• Data Encryption: Encrypt sensitive data in transit and at rest.

8. Future Enhancements

• Collaborative Features: Allow multiple users to collaborate on notes.
• Advanced Search: Implement advanced search capabilities based on embeddings.

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