biolinkml is a general purpose modeling language following object-oriented and ontological principles. Models are authored in YAML. A variety of artefacts can be generated from the model, including ShEx, JSON-Schema, OWL, Python dataclasses, UML diagrams, Markdown pages for deployment in a GitHub pages site, and more.
biolinkml is used for development of the BioLink Model, but the framework is general purpose and can be used for any kind of modeling.
Quickstart docs:
- Browse the model (biolinkml is self-describing): https://biolink.github.io/biolinkml/docs
- class definition Class definitions
- slot definition Class properties
- type definition Data types
- schema definition Schema definition
For an example, see the Jupyter notebook example
> pipenv install biolinkml- polymorphism/inheritance, see is_a
- abstract and mixin classes
- control JSON-LD mappings to URIs via prefixes declarations
- ability to refine meaning of a slot in the context of a particular class via slot usage
biolinkml can be used as a modeling language in its own right, or it can be compiled to other schema/modeling languages
We use a basic 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: trueNote this schema does not illustrate the more advanced features of blml
JSON Schema is a schema language for JSON documents
pipenv run gen-json-schema examples/organization.yaml
See examples/organization.schema.json
pipenv run gen-py-classes examples/organization.yaml > examples/organization.py
For example:
@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)ShEx - Shape Expressions Langauge
pipenv run gen-shex examples/organization.yaml > examples/organization.shex
See examples/organization.shex
pipenv run gen-markdown examples/organization.yaml -d examples/organization-docs/
This will generate a markdown document for every class and slot in the model
- YUML - UML diagram drawing tool
- Class and interface definitions for GraphQL
- Graphviz -- fairly basic representation of hierarchies
- Protobuf
- JSON and JSON-LD
- Markdown - markup language used by github and others
- OWL - Web Ontology Language
- RDF - Resource Description Format
These are specified using First Order Logic (FOL) axioms. See the semantics folder
Why invent our own yaml and not use JSON-Schema, SQL, UML, ProtoBuf, OWL, ...
each of these is tied to a particular formalisms. E.g. 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, e.g https://github.com/common-workflow-language/schema_salad
It may be possible to align with these.
Here X may be bioschemas, some upper ontology (BioTop), UMLS metathesaurus, bio*, 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.
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
[A Github action] is set up to automatically release the Pypi package. 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.