aerithnetzer/eis-graph-triples

EIS Knowledge Graph Triples

DRAFT

This project is representative of a "working in the open" ethos. It should not be used for training any machine-learning algorithm. What it should be used for is prototyping a vision of how future students will interact with collections.

RDF triples extracted by Claude from 1,493 OCR'd U.S. Environmental Impact Statements (mostly 1970s-era scans), plus cross-document entity resolution and EIS-to-EIS links.

Facts were extracted automatically and should be treated as leads to verify against the evidence quotes and the source documents.

Files

File	Rows	Contents
triples.parquet	883,344	One row per quad (subject, predicate, object, named graph)
documents.parquet	1,493	One row per EIS: barcode, label, source URI
entities.parquet	118,096	One row per entity: URI, type, label, alternate labels
graph.trig	—	The full RDF dataset in TriG format (named graphs)

Named graphs

• id:graph/doc/<barcode> (547,874 quads) — facts asserted by one EIS. The graph name is the provenance.
• id:graph/entities (331,527) — rdf:type, rdfs:label and skos:altLabel for every entity.
• id:graph/doclinks (3,823) — EIS-to-EIS relations, and owl:sameAs links for references that resolve to an EIS in the set.
• id:graph/ontology (120) — class and property declarations.

triples columns

Column	Description
graph	Named graph
barcode	Source EIS barcode (null outside per-document graphs)
subject, subject_label	Subject URI and its label
predicate	Predicate URI
object, object_label	Object URI or literal value, and its label when it is an entity
object_is_literal	True when object is a literal value
evidence	Verbatim quotes from the source document supporting the relation

URIs are shortened: eis: = https://example.org/eis/ (vocabulary) and id: = https://example.org/eis/id/ (instances).

Usage

from datasets import load_dataset

triples = load_dataset("aerithnetzer/eis-graph-triples", "triples", split="train")
docs = load_dataset("aerithnetzer/eis-graph-triples", "documents", split="train")

from pyoxigraph import Store, RdfFormat
store = Store()
store.bulk_load(path="graph.trig", format=RdfFormat.TRIG)