This document provides an overview of LinkML, a lightweight modeling language for building data schemas and knowledge graphs. It discusses how LinkML allows users to model data in a simple yet expressive way and generate outputs like JSON Schema, OWL, and RDF. LinkML aims to be developer-friendly and integrates with popular tools and standards. Several key projects currently use LinkML for tasks like building knowledge graphs and modeling genomics and clinical data.

1. LinkML: A brief guide for Monarch team
members
Chris Mungall
Monarch Initiative
cjmungall@lbl.gov
Monarch Data Call Sep 2021

2. Data modeling is ubiquitous in Monarch and Phen1
Ontology
developer
How do infectious
diseases (MONDO) relate
to infectious agents
(NCBITaxon)? Or to
treatments (MAXO) or
exposures (ECTO) Phenopacket Team
KG/Ingest
Engineer
How do
diseases relate
to genes?
How do patients
relate to
diseases? Or to
samples/.
UI developer
How do disease
pages relate to
gene pages?

3. We love our stacks
Ontology
developer
I ❤️ OWL
I ❤️ DOSDPs
I ❤️ ROBOT templates
Phenopacket Team
KG/Ingest
Engineer /
Graph ML
I ❤️ KGX
I ❤️ TSVs
I ❤️ Neo4J
I ❤️ Protobuf

4. Monarch-adjacent and beyond
Translator
How do genes relate
to chemicals
CRDC-H; NMDC
How do I relate
samples to
ontological
descriptors
CD2H / N3C
How do patients
relate to drug
treatments?
Allen / HCA
How do cell
types relate to
genes?
OBO / RO /
COB
How do
processes
relate to inputs

5. Monarch-adjacent and beyond
Semweb developer
I ❤️ RDF + triplestores
I ❤️ SHACL/ShEx
Clinical informatics
I ❤️ FHIR
Biologists
I ❤️ spreadsheets
CD2H / N3C
I ❤️ OMOP
GA4GH, HCA, many
devs
I ❤️ JSON-Schema
I ❤️ SQLite

6. LinkML: One ring to bind them….
https://linkml.io/ * https://github.com/linkml/linkml/

8. LinkML philosophy: Parasitize rather than compete
Strategy
● Be expressive enough to cover all our use cases
● Allow compilation to people’s favored stack (e.g JSON-Schema)
● Simple to do simple things, but add-ons where necessary
● Stealth Semantics
○ Everything can be RDF/Linked Data - if you want it to be

9. LinkML parasitizes other toolchains
YourModel
Documentation
OWL
JSON Schema
ShEx Schema
Schema.py
Object model
GraphQL Schema
Your LinkML Schema
(YAML)
JSONLD Context
. . .
LinkML
parser
Philosophy:
● Be expressive
● Parasitize
● Be developer-friendly
● Stealth semantics
● 80% rule

10. LinkML != Biolink Model
Biolink Model
● Expressed using LinkML
● An uber-data model for biology
○ Main types: gene, chemical, disease, …
○ Translator
○ Monarch KG
○ KG-COVID-19
○ KG-Microbe
○ KG-OBO
● Not appropriate for everything
○ Highly patient specific (CCDH, Pfx, FHIR)
○ Sample and omics data (CCDH, NMDC)
○ Single-cell data
LinkML
● A modeling language
● Can express multiple datamodels

11. id: https://example.org/linkml/hello-world
title: Really basic LinkML model
name: hello-world
license: https://creativecommons.org/publicdomain/zero/1.0/
version: 0.0.1
prefixes:
linkml: https://w3id.org/linkml/
sdo: https://schema.org/
ex: https://example.org/linkml/hello-world/
default_prefix: ex
default_curi_maps:
- semweb_context
imports:
- linkml:types
classes:
Person:
description: Minimal information about a person
class_uri: sdo:Person
attributes:
id:
identifier: true
slot_uri: sdo:taxID
first_name:
required: true
slot_uri: sdo:givenName
multivalued: true
last_name:
required: true
slot_uri: sdo:familyName
knows:
range: Person
multivalued: true
slot_uri: foaf:knows
Metadata
Dependencies
Namespaces
Actual Model
A sample LinkML Schema
11

12. id: https://example.org/linkml/hello-world
title: Really basic LinkML model
name: hello-world
license: https://creativecommons.org/publicdomain/zero/1.0/
version: 0.0.1
prefixes:
linkml: https://w3id.org/linkml/
sdo: https://schema.org/
ex: https://example.org/linkml/hello-world/
default_prefix: ex
default_curi_maps:
- semweb_context
imports:
- linkml:types
classes:
Person:
description: Minimal information about a person
class_uri: sdo:Person
attributes:
id:
identifier: true
slot_uri: sdo:taxID
first_name:
required: true
slot_uri: sdo:givenName
multivalued: true
last_name:
required: true
slot_uri: sdo:familyName
knows:
range: Person
multivalued: true
slot_uri: foaf:knows
Metadata
Dependencies
Namespaces
Actual Model
LinkML RDF is hidden in plain sight
12
Reuse schema
elements from
core
vocabularies
FAIR
(specifically:
allows diverse
data to be
combined)

13. Sample model documentation output
https://hsolbrig.github.io/sample_model/docs
13

14. BASE <https://example.org/linkml/hello-world/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX sdo: <https://schema.org/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
<String> xsd:string
<Person> CLOSED {
( $<Person_tes> ( sdo:givenName @<String> + ;
sdo:familyName @<String> ;
foaf:knows @<Person> *
) ;
rdf:type [ sdo:Person ]
)
}
"$id": "https://example.org/linkml/person",
"$schema": "http://json-schema.org/draft-07/schema#",
"definitions": {
"Person": {
"additionalProperties": false,
"description": "Minimal information about a person",
"properties": {
"first_name": {
"items": {
"type": "string"
},
"type": "array"
},
"id": {
"type": "string"
},
...
Shape Expressions (ShEx) Schema
JSON Schema
type Person
{
id: String!
firstName: [String]!
lastName: String!
knows: [Person]
}
Graphql Schema
Sample LinkML generated schemas
14

15. LinkML can also emit OWL
15

16. LinkML can also emit OWL
16

17. from examples.basic import Person
from linkml.dumpers import json_dumper, rdf_dumper
sam = Person("1172438", first_name=["Samual", "J"], last_name="Snooter")
ann = Person("17a3923", first_name="Jill", last_name="Jones", knows=[sam.id])
print(json_dumper.dumps(ann))
print(yaml_dumper.dumps(ann))
print(rdf_dumper.dumps(ann, contexts="../examples/jsonld/basic.context.jsonld"))
{
"id": "17a3923",
"first_name": [
"Jill"
],
"last_name": "Jones",
"knows": [
"1172438"
],
"@type": "Person"
}
id: 17a3923
first_name:
- Jill
last_name: Jones
knows:
- '1172438'
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix sdo: <https://schema.org/> .
<https://example.org/linkml/hello-world/17a3923> a sdo:Person ;
foaf:knows <https://example.org/linkml/hello-
world/1172438> ;
sdo:familyName "Jones" ;
sdo:givenName "Jill" .
python
JSON output YAML output RDF output (by way of JSON-LD)
Objects can be exported as JSON, YAML, or RDF
17

18. from linkml.loaders import yaml_loader
fred = yaml_loader.load('input/fred.yaml', target_class=Person)
print(fred.first_name)
['Fred', 'William']
harvey = json_loader.load('https://raw.githubusercontent.com/hsolbrig/linkml-enhanced-
template/master/tests/input/harvey.json', target_class=Person)
print(harvey.last_name)
Mackerson
ann = rdf_loader.load('input/ann.xml', target_class=Person, fmt="xml")
print(ann.last_name)
Richardson
id: 118-28-3199
first_name:
- Fred
- William
last_name: Phillips
knows:
- '1172438'
- '1172438'
input/fred.yaml
Python code
{
"id": "118-78-0697",
"first_name": [
"Harvey"
],
"last_name": "Mackerson"
}
http://example.org/.../harvey.json input/ann.xml
Objects can be imported from JSON, YAML, or RDF
18
<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:sdo="https://schema.org/"
>
<rdf:Description rdf:about="https://peoples.r.us">
<sdo:givenName>Ann</sdo:givenName>
<rdf:type rdf:resource="https://schema.org/Person"/>
<sdo:familyName>Richardson</sdo:familyName>
<sdo:givenName>Elizabeth</sdo:givenName>
</rdf:Description>
</rdf:RDF>

19. Class:
CyberAttack:
attributes:
has_attack_means:
AttackMeansEnum
enums:
AttackMeansUnum:
description: |-
Type of cyber attack
permissible_values:
BufferOverFlow:
SynFlood:
MaliciousCodeExecution:
...
Simple Enums
19
LinkML has rich enum support

20. Prefixes:
uco: http://ffrdc.ebiquity.umbc.edu/ns/ontology/#
classes:
CyberAttack:
attributes:
has_attack_means:
AttackMeansUnum
enums:
AttackMeansUnum:
description: |-
Type of cyber attack
permissible_values:
BufferOverFlow:
meaning: uco#BufferOverFlow
SynFlood:
meaning: uco#SynFlood
MaliciousCodeExecution:
meaning: uco#MaliciousCodeExecution
...
Ontological Enums
20
LinkML has rich enum support

21. Uses of LinkML
Lightweight data
dictionary for CSVs /
Machine Learning
Knowledge Graph
schemas
Simple Complex
Standard Databases

22. https://linkml.io/
linkml-registry/registry

23. Currently in use for: key projects
National Microbiome Data Collaborative
● Samples
● Omics datatypes
Genomics Standards Consortium
Translator (BioLink)
All our KGs (BioLink + Source modeling)
Alliance of Genome Resources
Center for Cancer Data Harmonization
Knowledge Graph Change Language (KGCL)
Chemical Ontology Schema
SSSOM
GHGPA
Critical Path Institute
...and more

24. Active development
https://github.com/topics/linkml

25. External tooling: DataHarmonizer driven by LinkML
https://genepio.org/DataHarmonizer/main.html -- spiritual successor to Phenote

26. Caveats / expectation management
The core is stable
● i.e. schemas won’t break in linkml1.x.x
series
● Used in production in multiple projects
Some things are currently incomplete
● Mapping to JSON-Schema
● Mapping to JSON-LD Contexts
● Documentation
New language features being added
● E.g constraint language, mapping
language
● These are extensions and won’t break
existing schemas
The tool stack is constantly evolving
● Other frameworks -> LinkML
● Automated schema mapping
● Generators for other languages
○ new : javagen
● Binding to databases
○ SQL, SPARQL, Solr, MongoDB, ...

27. Tutorial
https://linkml.io/linkml/intro/tutorial.html