github.com/DataTreehouse/maplib @v0.20.19

maplib: High-performance RDF knowledge graph construction, SHACL validation, SPARQL and Datalog in Python

maplib is written in Rust, it is built on Apache Arrow using Pola.rs and uses libraries from Oxigraph for handling linked data as well as parsing SPARQL queries.

maplib allows you to leverage your existing skills with Pandas or Polars to extract and wrangle data from existing databases and spreadsheets, before applying simple templates to them to build a knowledge graph. You can also read knowledge graphs extremely quickly from a wide variety of serialization formats. Using the built-in SPARQL, SHACL and Datalog engines means you can query, inspect, enrich and validate and then serialize the knowledge graph immediately. All query results are Polars Dataframes that are transferred zero-copy from Rust to Python. Currently, maplib is in-memory and supports around 100M triples on 32GB of RAM.

The core functionality of maplib (mapping, querying, serialization) is open source, but SHACL and Datalog functionality are not. Please send us a message, e.g. on LinkedIn (search for Data Treehouse) or on email (magnus at data-treehouse.com) if you want to try out these features. See our roadmap for upcoming features.

Installing

The package is published on PyPi and the API documented here:

pip install maplib

Model

We can easily map DataFrames to RDF-graphs using the Python library. Below is a reproduction of the example in the paper [1]. Assume that we have a DataFrame given by:

import polars as pl
pl.Config.set_fmt_str_lengths(150)

pi = "https://github.com/DataTreehouse/maplib/pizza#"
df = pl.DataFrame({
    "p":[pi + "Hawaiian", pi + "Grandiosa"],
    "c":[pi + "CAN", pi + "NOR"],
    "ings": [[pi + "Pineapple", pi + "Ham"],
             [pi + "Pepper", pi + "Meat"]]
})
print(df)

That is, our DataFrame is:

Then we can define a OTTR template, and create our knowledge graph by expanding this template with our DataFrame as input:

from maplib import Model, Prefix, Template, Argument, Parameter, Variable, RDFType, Triple, a
pi = Prefix(pi)

p_var = Variable("p")
c_var = Variable("c")
ings_var = Variable("ings")

template = Template(
    iri= pi.suf("PizzaTemplate"),
    parameters= [
        Parameter(variable=p_var, rdf_type=RDFType.IRI()),
        Parameter(variable=c_var, rdf_type=RDFType.IRI()),
        Parameter(variable=ings_var, rdf_type=RDFType.Nested(RDFType.IRI()))
    ],
    instances= [
        Triple(p_var, a, pi.suf("Pizza")),
        Triple(p_var, pi.suf("fromCountry"), c_var),
        Triple(
            p_var, 
            pi.suf("hasIngredient"), 
            Argument(term=ings_var, list_expand=True), 
            list_expander="cross")
    ]
)

m = Model()
m.map(template, df)
hpizzas = """
    PREFIX pi:<https://github.com/DataTreehouse/maplib/pizza#>
    CONSTRUCT { ?p a pi:HeterodoxPizza } 
    WHERE {
        ?p a pi:Pizza .
        ?p pi:hasIngredient pi:Pineapple .
    }"""
m.insert(hpizzas)
return m

We can immediately query the mapped knowledge graph:

m.query("""
PREFIX pi:<https://github.com/DataTreehouse/maplib/pizza#>
SELECT ?p ?i WHERE {
?p a pi:Pizza .
?p pi:hasIngredient ?i .
}
""")

The query gives the following result (a DataFrame):

Next, we are able to perform a construct query, which creates new triples but does not insert them.

hpizzas = """
PREFIX pi:<https://github.com/DataTreehouse/maplib/pizza#>
CONSTRUCT { ?p a pi:UnorthodoxPizza } 
WHERE {
    ?p a pi:Pizza .
    ?p pi:hasIngredient pi:Pineapple .
}"""
res = m.query(hpizzas)
res[0]

The resulting triples are given below:

If we are happy with the output of this construct-query, we can insert it in the model state. Afterwards we check that the triple is added with a query.

m.insert(hpizzas)
m.query("""
PREFIX pi:<https://github.com/DataTreehouse/maplib/pizza#>

SELECT ?p WHERE {
?p a pi:UnorthodoxPizza
}
""")

Indeed, we have added the triple:

API

The API is simple, and contains only one class and a few methods for: - expanding templates - querying with SPARQL - validating with SHACL - reading JSON to triples using Façade-X - importing triples (Turtle, RDF/XML, NTriples, JSON-LD, HDT) - writing triples (Turtle, RDF/XML, NTriples) - creating a new Model from a named graph

The API is documented HERE

Roadmap of features and optimizations

Spring 2026 - SHACL Rules - Disk based storage and internal serialization format - Jelly

Roadmap is subject to changes,particularly user and customer requests.

References

There is an associated paper [1] with associated benchmarks showing superior performance and scalability that can be found here. OTTR is described in [2].

[1] M. Bakken, "maplib: Interactive, literal RDF model model for industry," in IEEE Access, doi: 10.1109/ACCESS.2023.3269093.

[2] M. G. Skjæveland, D. P. Lupp, L. H. Karlsen, and J. W. Klüwer, “Ottr: Formal templates for pattern-based ontology engineering.” in WOP (Book), 2021, pp. 349–377.

Contributing

Any contributions will fall under the Apache 2.0 license of maplib.

Licensing

All code produced since August 1st. 2023 is copyrighted to Data Treehouse AS with an Apache 2.0 license unless otherwise noted.

All code which was produced before August 1st. 2023 copyrighted to Prediktor AS with an Apache 2.0 license unless otherwise noted, and has been financed by The Research Council of Norway (grant no. 316656) and Prediktor AS as part of a PhD Degree. The code at this state is archived in the repository at https://github.com/magbak/maplib.