Loka — RDF-star world-model corpus and checkpoints

Loka is a neuro-symbolic world model: an RDF-star triplestore engine plus a small role-aware transformer trained on the same triples, sharing a single SPARQL+ query layer. Generated triples write back into the store with propositionInferredFrom citation edges that point at the curated context the prediction was conditioned on, so every model-emitted fact is auditable, queryable, and filterable in SPARQL.

This dataset repo holds the corpus (label-substituted Wikidata slice) and the trained transformer checkpoints together so a corpus version and a checkpoint version stay aligned. The engine itself lives at https://github.com/EmmaLeonhart/Loka; the paper is at paper/paper.md in that repo.

Latest pinned model: loka-wikidata-v10 (revision tag v10, released 2026-05-11). See Snapshots below for the full version history.

Layout

Path	Contents
corpus/triples.txt	Tab-separated label-substituted triples (subject predicate object) used as training input. Wikidata QIDs/PIDs are substituted with English labels at preprocess time; @lang and ^^<datatype> suffixes are stripped from literals.
corpus/vocab_bpe.json	50 K-piece BPE vocabulary used by v6 and later.
corpus/tokenizer_bpe.json	tokenizers library JSON for the BPE tokenizer. Pass to inference / training scripts via --bpe-tokenizer.
corpus/vocab.json	Word-level vocabulary used by v3–v5 (kept for reproducibility).
corpus/generated_v*.nt	RDF-star inferences emitted by trained models, with propositionInferredFrom provenance back to their citation context.
loka-data/ (optional, ~770 MB)	The live RDF-star Loka store used to extract the corpus. Pull this and loka serve --data-dir loka-data/ to query directly.
checkpoints/wikidata_v*.pt	Role-aware transformer checkpoints, PyTorch .pt format. Same 44.5 M-parameter architecture from v5 onward (d_model 512, 6 layers, 8 heads, 50 K BPE vocab, 8 tokens per role).

Architecture

All current checkpoints (v5 onward) share:

• Role-aware masked S/P/O transformer. Input is a concatenation of three fixed-length slots (subject / predicate / object), each tagged with a role embedding. At training time one role is masked and the model predicts the original tokens; at inference the object slot is masked and decoded greedily with a cumulative repetition penalty.
• 44.5 M parameters. d_model=512, num_layers=6, nhead=8, tokens_per_role=8, max_len=28. Approx. 178 MB on disk per checkpoint.
• 50 K BPE vocabulary (v6+). v5 and earlier used a word-level regex tokenizer; that tokenizer dropped non-ASCII characters and is preserved only for back-comparison.

Datatype filtering policy

The training corpus is built from philippesaade/wikidata by training/preprocess.py in the Loka repo. Decisions per Wikidata datatype:

KEEP (semantic content; ~2,231 properties):

• wikibase-item — entity-to-entity links (label-substituted)
• wikibase-property — property-to-property links
• string — plain string values
• quantity — numeric values; leading + stripped (+1234 → 1234)
• time — dates; leading + stripped, Z dropped, T00:00:00 dropped when zero (+2012-10-15T00:00:00Z → 2012-10-15; BCE keeps the -)
• monolingualtext — value with @lang tag stripped. All languages kept in v7+ (v6 dropped non-English).

DROP (catalog cross-references or specialised noise; ~10,525 properties, about 82.5 % of all Wikidata property types):

• external-id (~10,206) — Freebase, ISNI, GND, LCCN, Dewey, etc. Training on these in v6 produced confident catalog-format hallucinations (ISNI -> "00000000") and a leak of the format shape onto unrelated predicates (instance of -> "+ Ġof - 00 - 03 T 00"). v7 onward exclude them.
• url (120), commonsMedia (91), math (36), wikibase-sense/wikibase-lexeme/wikibase-form/wikibase-entity-schema (47), globe-coordinate (10), geo-shape/musical-notation/tabular-data (15) — rare or non-transferable.

Full per-datatype spec: planning/wikidata-datatype-processing.md in the Loka repo. Property list pinned at training/wikidata_excluded_predicates.json.

Snapshots

Each meaningful checkpoint round is tagged. Pull a specific snapshot:

from huggingface_hub import hf_hub_download
ckpt = hf_hub_download(
    repo_id="EmmaLeonhart/loka",
    repo_type="dataset",
    filename="checkpoints/wikidata_v8.pt",
    revision="v8",
)
# also pull the matching tokenizer + vocab
tok = hf_hub_download(repo_id="EmmaLeonhart/loka", repo_type="dataset",
                      filename="corpus/tokenizer_bpe.json", revision="v8")
vocab = hf_hub_download(repo_id="EmmaLeonhart/loka", repo_type="dataset",
                        filename="corpus/vocab_bpe.json", revision="v8")

Tag	Date	Final ppl	Corpus	Notes
v3	2026-05-08	53.4	757 k noisy	First end-to-end run. Datatype-suffix leakage bug; emitted xmlschema decimal http www w3 org as memorised template.
v4	2026-05-09	92.5	757 k cleaned	16 M params. Datatype-suffix bug fixed. Mode collapse on common connectors (of of of of) addressed with decode-time cumulative repetition penalty.
v5	2026-05-09	84.85	757 k cleaned	44.5 M params (3 × scale-up). Bigger model picks specific entities (halle, 33, kosmos 116) where v4 fell back to fillers.
v6-bpe	2026-05-10	194.98	757 k cleaned	BPE tokenizer added. Same 44.5 M architecture. Final ppl not directly comparable to v5 (BPE has more tokens per role). Catalog hallucinations dominant in post-training behavioural tests — diagnosed as corpus composition.
v7	2026-05-10	192.63	184 k v7-cleaned	Catalog datatypes dropped (~76 % of v6 corpus removed). Same architecture, 5 epochs. Tied ppl with v6 on a 4 ×-smaller corpus, but the catalog-format leak (instance of -> "+ Ġof - 00 - 03 T 00") is gone.
v8	2026-05-10	64.65	184 k v7-cleaned	Same architecture, 20 epochs from scratch on the v7 corpus. 3 × ppl improvement over v7 — the v7 corpus was undersaturated at 5 epochs. Loss still descending at epoch 20, so the next bottleneck is data scale.

For the live latest list, see this dataset's Files and versions tab on Hugging Face. Tag main always tracks the most recent upload.

Generated-triple provenance

Every triple under corpus/generated_*.nt carries an RDF-star annotation block:

<S> <P> "predicted-value" .
<<S P "predicted-value">> loka-prov:propositionGenerated   "true"^^xsd:boolean .
<<S P "predicted-value">> loka-prov:propositionGeneratedBy "loka-wikidata-v8" .
<<S P "predicted-value">> loka-prov:propositionConfidence  "0.67"^^xsd:decimal .
<<S P "predicted-value">> loka-prov:propositionInferredFrom <<S P_existing O_existing>> .
... (one inferredFrom edge per cited context triple)

loka-prov: expands to http://loka.dev/provenance/. Predicates under that namespace are reserved system metadata — the model never sees them, never proposes them as candidate predicates, and never emits them, enforced at three layers (corpus stripping in training/preprocess.py, candidate filtering in training/infer_with_citations.py, emit-time guard before each primary triple is written).

The propositionInferredFrom edges are auditable like any other RDF: SPARQL can query "every generated triple that cites a triple about X" or "remove all v6 generations" with a single pattern match. Generated triples are flagged out of training corpora automatically (the FILTER NOT EXISTS << ?s ?p ?o >> loka-prov:propositionGenerated clause in TRAINING_CORPUS_QUERY).

Versioning policy

• main branch on this dataset always points at the most recent upload.
• Each numbered checkpoint round (v3, v4, v5, v6-bpe, v7, v8, …) is a stable tag. Pull revision="vN" for reproducibility.
• The local 12-hour cycle loop (tools/training_cron.py in the Loka repo) produces a new tagged snapshot each cycle. README is regenerated on every upload so the version history table above stays current.

License

Apache 2.0. Source data is Wikidata under CC0; the upstream philippesaade/wikidata snapshot is used unmodified for the import step.

Citation

If you use these checkpoints or the corpus, please cite:

@misc{loka2026,
  title = {Loka: Generative Citation in a Neuro-Symbolic World Model over RDF-Star Knowledge Graphs},
  author = {Leonhart, Emma},
  year = {2026},
  url = {https://github.com/EmmaLeonhart/Loka},
  note = {Paper at \url{https://github.com/EmmaLeonhart/Loka/blob/main/paper/paper.md}}
}