Skip to main content

Linked Open Data Modeling Language

Project description

Pyversions PyPi

Binder Link

LinkML - Linked data Modeling Language

LinkML is a general purpose modeling language following object-oriented and ontological principles. LinkML models can be specified in YAML, JSON or RDF.

A variety of artefacts can be generated from the model:

  • ShEx
  • JSON Schema
  • OWL
  • Python dataclasses
  • UML diagrams
  • Markdown pages (for deployment in a GitHub pages site)

...and more.

The documentation can also be viewed on the LinkML documentation.

You can browse the metamodel component documentation here. LinkML is self-describing, but a few important vocabulary terms to keep in mind are:

Further details about the general design of LinkML can be found in the LinkML Modeling Language Specification.

As an example, LinkML has been used for the development of the BioLink Model, but the framework itself is general purpose and can be used for any kind of modeling. For an example Biolink metamodel, see this Jupyter Notebook.

Installation

This project uses pipenv for installation. Some IDE's like PyCharm also have direct support for pipenv.

> pipenv install linkml

Language Features

  • Polymorphism/Inheritance, see is_a
  • Abstract and Mixin classes
  • Control JSON-LD mappings to URIs via prefix declarations
  • Ability to refine the meaning of a slot in the context of a particular class via slot usage

Examples

LinkML can be used as a modeling language in its own right, or it can be compiled to other schema/modeling languages.

We will use the following simple schema for illustrative purposes:

id: http://example.org/sample/organization
name: organization

types:
  yearCount:
    base: int
    uri: xsd:int
  string:
    base: str
    uri: xsd:string

classes:

  organization:
    slots:
      - id
      - name
      - has boss

  employee:
    description: A person
    slots:
      - id
      - first name
      - last name
      - aliases
      - age in years
    slot_usage:
      last name :
        required: true

  manager:
    description: An employee who manages others
    is_a: employee
    slots:
      - has employees

slots:
  id:
    description: Unique identifier of a person
    identifier: true

  name:
    description: human readable name
    range: string

  aliases:
    is_a: name
    description: An alternative name
    multivalued: true

  first name:
    is_a: name
    description: The first name of a person

  last name:
    is_a: name
    description: The last name of a person

  age in years:
    description: The age of a person if living or age of death if not
    range: yearCount

  has employees:
    range: employee
    multivalued: true
    inlined: true

  has boss:
    range: manager
    inlined: true

Note that this schema does not illustrate the more advanced datamodel features like in Biolink Model.

Generators

JSON Schema

JSON Schema is a schema language for JSON documents.

With the example organization LinkML schema schema, we can illustrate the autogeneration of a JSON Schema output. You can run:

pipenv run gen-json-schema examples/organization.yaml

Note that any JSON that conforms to the derived JSON Schema can be converted to RDF using the derived JSON-LD context.

JSON-LD Context

JSON-LD context provides mapping from JSON to RDF.

With the example organization LinkML schema schema, we can illustrate the autogeneration of a JSON-LD context output. You can run:

pipenv run gen-jsonld-context examples/organization.yaml

You can control the output via prefixes declarations and default_curi_maps.

Any JSON that conforms to the derived JSON Schema (see above) can be converted to RDF using this context.

You can also combine a JSON instance file with a JSON-LD context using simple code or a tool like jq:

jq -s '.[0] * .[1]' examples/organization-data.json examples/organization.context.jsonld > examples/organization-data.jsonld

The above generated JSON-LD file can be converted to other RDF serialization formats such as N-Triples. For example we can use Apache Jena as follows:

riot examples/organization-data.jsonld > examples/organization-data.nt

Python Dataclasses

With the example organization LinkML schema schema, we can illustrate the autogeneration of a Python Dataclass output. You can run:

pipenv run gen-py-classes examples/organization.yaml > examples/organization.py
Python Dataclass for `organization` schema
@dataclass
class Organization(YAMLRoot):
    _inherited_slots: ClassVar[List[str]] = []

    class_class_uri: ClassVar[URIRef] = URIRef("http://example.org/sample/organization/Organization")
    class_class_curie: ClassVar[str] = None
    class_name: ClassVar[str] = "organization"
    class_model_uri: ClassVar[URIRef] = URIRef("http://example.org/sample/organization/Organization")

    id: Union[str, OrganizationId]
    name: Optional[str] = None
    has_boss: Optional[Union[dict, "Manager"]] = None

    def __post_init__(self, **kwargs: Dict[str, Any]):
        if self.id is None:
            raise ValueError(f"id must be supplied")
        if not isinstance(self.id, OrganizationId):
            self.id = OrganizationId(self.id)
        if self.has_boss is not None and not isinstance(self.has_boss, Manager):
            self.has_boss = Manager(self.has_boss)
        super().__post_init__(**kwargs)

For more details see PythonGenNotes.

The python object can be directly serialized as RDF.

ShEx

ShEx, short for Shape Expressions Language is a modeling language for RDF files.

With the example organization LinkML schema schema, we can illustrate the autogeneration of a ShEx output. You can run:

pipenv run gen-shex examples/organization.yaml > examples/organization.shex
ShEx output for `organization` schema
BASE <http://example.org/sample/organization/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX xsd1: <http://example.org/UNKNOWN/xsd/>


<YearCount> xsd1:int

<String> xsd1:string

<Employee>  (
    CLOSED {
       (  $<Employee_tes> (  <first_name> @<String> ? ;
             <last_name> @<String> ;
             <aliases> @<String> * ;
             <age_in_years> @<YearCount> ?
          ) ;
          rdf:type [ <Employee> ]
       )
    } OR @<Manager>
)

<Manager> CLOSED {
    (  $<Manager_tes> (  &<Employee_tes> ;
          rdf:type [ <Employee> ] ? ;
          <has_employees> @<Employee> *
       ) ;
       rdf:type [ <Manager> ]
    )
}

<Organization> CLOSED {
    (  $<Organization_tes> (  <name> @<String> ? ;
          <has_boss> @<Manager> ?
       ) ;
       rdf:type [ <Organization> ]
    )
}

OWL

Web Ontology Language OWL is modeling language used to author ontologies.

With the example organization LinkML schema schema, we can illustrate the autogeneration of a ShEx output. You can run:

pipenv run gen-owl examples/organization.yaml > examples/organization.owl.ttl
OWL output for `organization` schema
<http://example.org/sample/organization/Organization> a owl:Class,
        meta:ClassDefinition ;
    rdfs:label "organization" ;
    rdfs:subClassOf [ a owl:Restriction ;
            owl:onClass <http://example.org/sample/organization/String> ;
            owl:onProperty <http://example.org/sample/organization/id> ;
            owl:qualifiedCardinality 1 ],
        [ a owl:Restriction ;
            owl:maxQualifiedCardinality 1 ;
            owl:onClass <http://example.org/sample/organization/String> ;
            owl:onProperty <http://example.org/sample/organization/name> ],
        [ a owl:Restriction ;
            owl:maxQualifiedCardinality 1 ;
            owl:onClass <http://example.org/sample/organization/Manager> ;
            owl:onProperty <http://example.org/sample/organization/has_boss> ] .

Generating Markdown documentation

The below command will generate a Markdown document for every class and slot in the model which can be used in a static site for ex., GitHub pages.

pipenv run gen-markdown examples/organization.yaml -d examples/organization-docs/

Specification

See specification. Also see the semantics folder for an experimental specification in terms of FOL.

FAQ

Why not use X as the modeling framework?

Why invent our own yaml and not use JSON-Schema, SQL, UML, ProtoBuf, OWL, etc.?

Each of these is tied to a particular formalism. JSON Schema to trees. OWL to open world logic. There are various impedance mismatches in converting between these. The goal was to develop something simple and more general that is not tied to any one serialization format or set of assumptions.

There are other projects with similar goals for ex., schema_salad. It may be possible to align with these.

Why not use X as the datamodel?

Here X may be bioschemas, some upper ontology (BioTop), UMLS metathesaurus, bio*, and various other attempts to model all of biology in an object model.

Currently, as far as we know there is no existing reference datamodel that is flexible enough to be used here.

Biolink Modeling Language

Type Definitions

typeof:
    domain: type definition
    range: type definition
    description: supertype

  base:
    domain: type definition
    description: python base type that implements this type definition
    inherited: true

  type uri:
    domain: type definition
    range: uri
    alias: uri
    description: the URI to be used for the type in semantic web mappings

  repr:
    domain: type definition
    range: string
    description: the python representation of this type if different than the base type
    inherited: true

Slot Definitions

Developers Notes

Release to PyPI

A Github action is set up to automatically release the package to PyPI. When it is ready for a new release, create a Github release. The version should be in the vX.X.X format following the semantic versioning specification.

After the release is created, the GitHub action will be triggered to publish to Pypi. The release version will be used to create the Pypi package.

If the Pypi release failed, make fixes, delete the GitHub release, and recreate a release with the same version again.

Additional Documentation

LinkML for environmental and omics metadata

History

This framework used to be called BiolinkML. LinkML replaces BiolinkML. For assistance in migration, see Migration.md.

Example Projects

Note: this list will be replaced by the linkml registry

Project details


Release history Release notifications | RSS feed

This version

1.1.9

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

linkml-1.1.9.tar.gz (56.0 MB view hashes)

Uploaded Source

Built Distribution

linkml-1.1.9-py3-none-any.whl (131.8 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page