tranz

Knowledge graph embedding models.

[dependencies]
tranz = "0.5"

Dual-licensed under MIT or Apache-2.0.

Models

Each model scores a triple (head, relation, tail) differently:

$\mathbf{h}, \mathbf{r}, \mathbf{t}$ are learned embedding vectors for head, relation, and tail. $\lVert \cdot \rVert$ is the L2 norm, $\circ$ is element-wise product, $\langle \cdot \rangle$ is the trilinear dot product, $\bar{\mathbf{t}}$ is the complex conjugate.

Quick start

Install with cargo install tranz --features candle.

# Train with 1-N scoring (recommended)
tranz train --data data/WN18RR/ --model complex --dim 200 \
    --1n --label-smoothing 0.1 --reciprocals \
    --epochs 100 --lr 0.001 --output embeddings/ --eval

# Train with negative sampling (classic)
tranz train --triples my_graph.tsv --model transe --dim 200 \
    --epochs 500 --gamma 9.0 --alpha 0.5 --output embeddings/ --eval

# Predict from saved embeddings
tranz predict --embeddings embeddings/ --model distmult \
    --head "aspirin" --relation "treats" --k 10

Benchmark: WN18RR

Published ComplEx MRR on WN18RR is 0.475 (Lacroix et al. 2018). tranz reaches 92% of published with the same recipe (Adagrad, N3, reciprocals).

Library usage

use tranz::{TransE, DistMult, Scorer};
use tranz::dataset::{load_dataset, FilterIndex, InternedDatasetExt};
use tranz::eval::evaluate_link_prediction;

// Load dataset (types from lattix::kge)
let ds = load_dataset("data/WN18RR".as_ref()).unwrap();
let mut interned = ds.into_interned();
interned.add_reciprocals();

// Create model and query
let model = DistMult::new(interned.num_entities(), interned.num_relations(), 200);
let top10 = model.top_k_tails(0, 0, 10);

// Evaluate (filtered link prediction)
let filter = FilterIndex::from_dataset(&interned);
let metrics = evaluate_link_prediction(&model, &interned.test, &filter, interned.num_entities());

Generic triple loading

use tranz::dataset::{Dataset, DatasetExt};

let ds = Dataset::load_flexible("my_graph.tsv".as_ref()).unwrap();
let ds = ds.split(0.1, 0.1); // 80/10/10
let interned = ds.into_interned();

Embedding export

use tranz::io::{export_embeddings, flatten_matrix};

// Export to w2v TSV
export_embeddings("output/".as_ref(), &names, &vecs, &rel_names, &rel_vecs).unwrap();

// Flat f32 matrix for FAISS/Qdrant
let flat: Vec<f32> = flatten_matrix(&vecs);

Training (requires candle feature)

Two training modes:

1-N scoring (recommended): scores all entities per query via matmul + BCE loss. Faster convergence, no negative sampling noise.

Negative sampling (classic): samples k negatives per positive with self-adversarial weighting (SANS).

use tranz::train::{train, TrainConfig, ModelType};

let config = TrainConfig {
    model_type: ModelType::DistMult,
    dim: 200,
    one_to_n: true,
    label_smoothing: 0.1,
    embedding_dropout: 0.1,
    epochs: 100,
    ..TrainConfig::default()
};

let result = train(&triples, num_entities, num_relations, &config, &device).unwrap();

Companion to subsume

subsume embeds entities as geometric regions (boxes, cones) where containment encodes subsumption. tranz embeds entities as points where distance/similarity encodes relational facts.

• subsume: ontology completion, taxonomy expansion, logical query answering
• tranz: link prediction, relation extraction, knowledge base completion