Specification for Graphical Neural Network Model

Specification for Freelance Graphical Neural Network Model

1. Objective:
You are tasked with creating a graphical neural network to represent the relationships between words, roots, and their semantic categories in Quranic Arabic. The model should represent the structure outlined in the research paper, using Graph Neural Networks (GNN) and specifically Relational Graph Convolutional Networks (R-GCNs). This model will help illustrate semantic relationships such as derivation, co-occurrence, synonyms, antonyms, and hyponyms.

2. Deliverables:

Graphical representation of the neural network

-Graph structure
-Code for the construction
-Example Graph
-Visualization of the Embedding Space

Output: Provide a set of learned node embeddings that can be used for:

-Semantic search
-Contextual retrieval

3. Platforms and Tools:

-GitHub

File Format: The output graph should be in a graph visualization format (e.g., .dot, .graphml, or .png for visual representation). Embedding vectors could be provided as .csv or .txt files for easier use.

Environment: The code should be compatible with Python 3.x and utilize PyTorch, PyTorch Geometric, and NetworkX. Include a requirements.txt file for easy installation of dependencies.

4. Expected Outputs:

-Graphical Output
-Include a small example
-Provide node embeddings of words and their relationships in a text or CSV format.
-Show clustering results of semantically related words in a 2D plot or other appropriate form.
-Clustering Analysis:
-Code for Graph Construction

Data Source: 2 data sources available online