qinghuiwan/structural-isomorphism-v1

structural-isomorphism/structural-v1

A sentence-transformer model fine-tuned for structural similarity -- recognizing that phenomena from completely different domains share the same underlying structure.

Unlike standard semantic similarity models that match by surface vocabulary, this model maps descriptions with the same structural pattern close together in embedding space, regardless of domain.

Model Description

• Base model: shibing624/text2vec-base-chinese (BERT-based, 768-dim)
• Training data: SIBD -- 1,214 descriptions across 84 structural types
• Training objective: MultipleNegativesRankingLoss (positive pairs = same structural type, different domain)
• Epochs: 10 | Batch size: 16 | Learning rate: 2e-5 | Warmup: 10%

Evaluation Results

Metric	Base Model	This Model	Improvement
Silhouette Score	-0.012	0.847	+0.859
Retrieval@5	20.3%	100.0%	+79.7%
Retrieval@10	18.0%	100.0%	+82.0%
Intra-class Similarity	0.643	0.933	+0.290
Inter-class Similarity	0.569	0.174	-0.395

Usage

With sentence-transformers

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer("structural-isomorphism/structural-v1")

# Encode two descriptions from different domains
emb1 = model.encode("A thermostat detects low temperature and turns on heating")
emb2 = model.encode("The pancreas detects high blood sugar and releases insulin")

similarity = util.cos_sim(emb1, emb2).item()
print(f"Structural similarity: {similarity:.3f}")
# Both are negative feedback loops -> high similarity

With the search engine

from structural_isomorphism import StructuralSearch

search = StructuralSearch()
results = search.query("Small input causes disproportionately large output")
for r in results[:5]:
    print(f"{r['name']} ({r['domain']}) - {r['score']:.3f}")

Intended Use

• Cross-domain structural similarity search
• Finding analogies and inspiration across fields
• Scientific discovery: identifying unknown structural connections
• Educational tools for teaching structural thinking

Limitations

• Language: Currently trained on Chinese text only
• Domain coverage: 84 structural types may not cover all possible patterns
• The model recognizes structural types present in training data; novel structural types may not be well represented

Citation

@article{structural-isomorphism-2026,
  title={Structural Isomorphism Search: Cross-Domain Structural Similarity Retrieval via Fine-tuned Embeddings},
  author={Wan, Qihang},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2026}
}

License

MIT