ellisdoro/agro-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early-on2vec-koji-early

agro_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early

This is a sentence-transformers model created with on2vec, which augments text embeddings with ontological knowledge using Graph Neural Networks.

Model Details

• Base Text Model: all-MiniLM-L6-v2
  • Text Embedding Dimension: 384
• Ontology: agro.owl
• Domain: general
• Ontology Concepts: 4,162
• Concept Alignment: 4,162/4,162 (100.0%)
• Fusion Method: additive
• GNN Architecture: GCN
• Structural Embedding Dimension: 4162
• Output Embedding Dimension: 64
• Hidden Dimensions: 512
• Dropout: 0.0
• Training Date: 2025-09-19
• on2vec Version: 0.1.0
• Source Ontology Size: 7.2 MB
• Model Size: 120.6 MB
• Library: on2vec + sentence-transformers

Technical Architecture

This model uses a multi-stage architecture:

1. Text Encoding: Input text is encoded using the base sentence-transformer model
2. Ontological Embedding: Pre-trained GNN embeddings capture structural relationships
3. Fusion Layer: Simple concatenation of text and ontological embeddings

Embedding Flow:

• Text: 384 dimensions → 512 hidden → 64 output
• Structure: 4162 concepts → GNN → 64 output
• Fusion: additive → Final embedding

How It Works

This model combines:

1. Text Embeddings: Generated using the base sentence-transformer model
2. Ontological Embeddings: Created by training Graph Neural Networks on OWL ontology structure
3. Fusion Layer: Combines both embedding types using the specified fusion method

The ontological knowledge helps the model better understand domain-specific relationships and concepts.

Usage

from sentence_transformers import SentenceTransformer

# Load the model
model = SentenceTransformer('agro_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early')

# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)

# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])

Training Process

This model was created using the on2vec pipeline:

1. Ontology Processing: The OWL ontology was converted to a graph structure
2. GNN Training: Graph Neural Networks were trained to learn ontological relationships
3. Text Integration: Base model text embeddings were combined with ontological embeddings
4. Fusion Training: The fusion layer was trained to optimally combine both embedding types

Intended Use

This model is particularly effective for:

• General domain text processing
• Tasks requiring understanding of domain-specific relationships
• Semantic similarity in specialized domains
• Classification tasks with domain knowledge requirements

Limitations

• Performance may vary on domains different from the training ontology
• Ontological knowledge is limited to concepts present in the source OWL file
• May have higher computational requirements than vanilla text models

Citation

If you use this model, please cite the on2vec framework:

@software{on2vec,
  title={on2vec: Ontology Embeddings with Graph Neural Networks},
  author={David Steinberg},
  url={https://github.com/david4096/on2vec},
  year={2024}
}

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Created with on2vec 🧬→🤖