Ontologies 101Melissa Haendel and Jim BalhoffINCF taskforce cross program meeting

Stockholm, Aug. 30, 2013

All about ontologies

1. What is an ontology?2. A little logic3. Upper ontologies4. OWL and RDF5. Data integration

MouseEcotope GlyProt

DiabetInGene

GluChem

sphingolipid transporter

activity

Common controlled vocabularies indicate the same meaning under different annotation circumstances

Any closed, prescribed list of terms used for classifying data

What is a controlled vocabulary?

Key Features: Terms are not usually defined. Relationships between the terms are

not usually defined. Can be a list.

Here is a CV of wines:Pinot noir, red, chardonnay, Chianti, Bordeaux, Riesling….These are all different types- color, location, varietal, and are present in a list.

Another example would be the map locations list at the end of your Gazeteer.

Any controlled vocabulary that is arranged in a hierarchy

What is a Taxonomy?

Key Features:• Terms are not usually defined.• Relationships between the terms are not usually defined.• Terms are arranged in a hierarchy.

Here is a wine taxonomy:

WineRed

merlotzinfandelcabernetpinot noir

Whitechardonnaypinot grisRiesling

A taxonomy that contains additional information about use of the terms

What is a Thesaurus?

Key Features:• Terms are not usually defined.• Relationships between the terms are not usually defined.• Terms are arranged in a hierarchy.• Statements about the terms are included such as scope notes or instructions for use.

Some well known thesauri are:WordNet, NCI cancer thesaurus, MeSH

A formal conceptualization of a specified domain of interestWhat is an ontology?

Key Features:• Terms are defined.• Relationships between the terms are defined, allowing logical inference.• Terms are arranged in a hierarchy.• Expressed in a knowledge representation language such as RDFS, OBO, or OWL.

Some well known ontologies are:SnoMED, Foundational Model of Anatomy, Gene Ontology, Linnean Taxonomy of species

Reproduced with permission, Jason Freenyhttp://web.mac.com/moistproduction/flash/index.html

http://www.mkbergman.com/?m=20070516

The Ontology spectrum:

Bottom line: you get what you pay for.

OBO

Are ontologies about terms or things?What matters are things

Data annotated to the right schema will be more consistent

DessertIce cream

gelatosno-conesoft-servecustard cream

Caketortedouble-layergaletteangel food cake

DES:000038DES:000034

DES:000456DES:005566DES:000564DES:000744

DES:0003221DES:000667DES:000975DES:000544DES:000765

DES:000034 - Frozen milk and/or cream with various sugars, and flavoring such as fresh fruit and nut purees.[1]

Labels are handles, not things

Why build an ontology? A simple example

Number of genes annotated to each of the following brain parts in an ontology:

brain 20part_of hindbrain 15part_of rhombomere 10

Query brain without ontology 20Query brain with ontology 45

Ontologies can facilitate grouping and retrieval of data

is_a

entity

organism

cat

mammal

animalis_a

is_a

is_ahuman

is_a

instance_of instance_of

Peanut Chris Shaffer

Classes, subclasses, and instances

Subtyping relation

is_a = SubClassOf

OWL individuals:

How do you tell if it is an instance or a class?Is there more than one in existence? Is the entity referencing a group of things with common properties?

Class or instance?There is only one Snoopy

There is a class of things labeled “Snoopy toys”

Class or instance? Class or instance?

There is only one Alaska

There is a class of things labeled “States”

There is only one blue morpho in my specimen collection

There is a class of things labeled “Blue morpho butterflies”

General Principle for Logical DefinitionsDefinitions are of the following Genus-Differentia form:

X = a Y which has one or more differentiating characteristics.

where X is the is_a parent of Y.

Definition: Blue cylinder = Cylinder that has color blue.

Definition: cylinder = Surface formed by the set of lines perpendicular to a plane, which pass through a given circle in that plane. is_a is_a

Definition: Red cylinder = Cylinder that has color red.

The True Path Rule

cuticle synthesis--[i] chitin metabolismcell wall biosynthesis--[i] chitin metabolism----[i] chitin biosynthesis----[i] chitin catabolism

chitin metabolism--[i] chitin biosynthesis--[i] chitin catabolism--[i] cuticle chitin metabolism----[i] cuticle chitin biosynthesis----[i] cuticle chitin catabolism--[i] cell wall chitin metabolism----[i] cell wall chitin biosynthesis----[i] cell wall chitin catabolism

GO Before: GO After:

BUT: A fly chitin synthase gene could be annotated to chitin biosynthesis, and appear in a query for genes annotated to cell wall biosynthesis (and its children), which makes no sense because flies don't have cell walls.

NOW: all the subClass terms can be followed up to chitin metabolism, but cuticle chitin metabolism terms do not trace back to cell wall terms, so all the paths are true.

The pathway from a subClass all the way up to its top level parent(s) must be universally true.

Where does the True Path Rule come from?Transitivity. Some relations are transitive, and apply across all levels of the hierarchy.

For example, a cat is_a mammal, and a mammal is_a vertebrateSOa cat is_a vertebrate

=> This is the true path rule and is because the is_a relation is transitive.

Some properties are not transitive.

For example, head has_quality round.and,

head part_of organism. So is the organism round? Of course not!

BUT, eyes are part_of head, and head part_of organism, SO eye part_of organism is true, because part_of is a tranistive relation.

Relations are logically defined in a common relation ontology or within each ontology that uses them.

≠>

Relationships and definitions

A relationship from one class to another is a formalized part of its definition (an object property restriction in OWL)

A subtype relation (is_a in OBO, SubClassOf in OWL) specifies necessary conditions for membership of a class.

For example, finger part_of hand (all finger part_of some hand) states that a necessary condition of being in the class finger is to be part of some hand.

So… if a finger exists, it is part of some hand. But…this does not mean that if a hand exists, it has as a part a finger.

There are many useful ways to classify parts of organisms:

its parts and their arrangement its relation to other structures

what is it: part of; connected to; adjacent to, overlapping?

its shape its function its developmental origins its species or clade its evolutionary history

Cajal 1915, “Accept the view that nothing in nature is useless, even from the human point of view.”

Not all classification is useful

Be practical: Build ontologies for what you need and for what can be reused

About thirty years ago there was much talk that geologists ought only to observe and not theorise; and I well remember some one saying that at this rate a man might as well go into a gravel-pit and count the pebbles and describe the colours.

C. Darwin

neuron

anatomical structure

lumen

anatomical space

epidural space

anatomical space

anatomical structure

✗

Some classes are declared to never share any instances in common

OWL DisjointWith OBO: disjoint_from

NO!

About reasonersA piece of software able to infer logical consequences from a set of asserted facts or axioms.

They are used to check the logical consistency of the ontologies and to extend the ontologies with "inferred" facts or axioms

For example, a reasoner would infer:

Major premise: All mortals die.Minor premise: Some men are mortals.Conclusion: Some men die.

Different reasoners can perform slightly differently. There are a number of reasoners to be aware of: ELK, HermiT, Pellet, etc.

Different kinds of definitions An ontology is a collection of axioms

An axiom is simply a sentence or a statement Axioms can be

non-logical (aka “annotations” or “text definitions”)

• E.G. GO_0000262 has synonym ‘mtDNA’

• opaque to reasoners logical

• well-defined semantics

• understood by reasoners

• Example: SubClassOf axioms

• Arguments can be classes or class expressions (for example, the class of things that are parts of the midbrain)

Classifying anatomy

appendage

antenna forewing

wing

hindwing

Relationships record classifications too

substantia nigra

part_of some ‘midbrain’

trochlear nucleus

‘substantia nigra’ SubClassOf part_of some ‘midbrain’

The knowledge in an ontology can make the reasons for classification explicit

Any sense organ that functions in the detection of smell is an olfactory sense organ

sense organcapable_of some detection of smell

olfactory sense organ

nose

sense organ

nose

capable_of some detection of smell

Classifying

sense organcapable_of some detection of smell

olfactory sense organ

nose

=> These are necessary and sufficient conditions, also called an equivalent class axiom

Compositionality and avoiding asserted multiple inheritance

We can logically define composed classes and create complex definitions from simpler ones

aka: building blocks, cross-products, logical definitionsDescriptions can be composed at any time

Ontology construction time (pre-composition) Annotation time (post-composition)

Formal necessary and sufficient definitions + a reasoner

Automatic (and therefore manageable) classification Requires subtype classification, so apart from the root

term(s), no term should lack a superclass parent.

Let the reasoner do the work!

Post-composed versus pre-composed anatomical entities are logically equivalent

Named class: Plasma membrane of neuron

plasma membrane and part_of some neuron

Gene Ontology Relation Ontology Cell Ontology

Genus Differentia

Has same semantic meaning as:

chemical entities

Many perspectives, many ontologies – that overlap in content

grossanatomy

tissues

cellscellanatomy

proteins

phenotypes

clinical disorders

processes

physiological processes

development

reactions

cellular processes

behavior

evolutionary characters

nervous systemneural crest

Domain ontologies are organized according to upper ontologies (Basic Formal Ontology) that specify the general types of things that exist

RELATION TO TIME

GRANULARITY

CONTINUANT OCCURRENT

INDEPENDENT DEPENDENT

ORGAN ANDORGANISM

Organism(NCBI

Taxonomy)

Anatomical Entity

(FMA, CARO)

OrganFunction

(FMP, CPRO) Phenotypic Quality(PaTO)

Biological Process(GO)

CELL AND CELLULAR COMPONENT

Cell(CL)

Cellular Component(FMA, GO)

Cellular Function(GO)

MOLECULEMolecule

(ChEBI, SO,RnaO, PrO)

Molecular Function(GO)

Molecular Process(GO)

=> Classification according to these higher level types helps ensure the True Path Rule holds

The Common Anatomy Reference OntologyCARO is a structural classification based on

granularity

From the bottom up:Cell componentCellTissueMulti-tissue structure

From the top down:Organism subdivisionAnatomical system

Acellular structures

CARO is an upper reference ontology that can be used to structure new anatomy ontologies

Example of complexity arising from multiple species-contexts

erythrocyte

cell

nucleate cell enucleate cell

not applicable in all contexts

Example of complexity arising from multiple species-contexts

erythrocyte

nucleate erythrocyte

enucleate erythrocyte

cell

nucleate cell enucleate cell

zebrafish nucleate

erythrocyte

human erythrocyteZFA:0009256

… …

CL:0000562

CL:0000232

CL:0000592

FMA:81100

species ontologiesattached at appropriatelevel

Developmental Biology, Scott Gilbert, 6th ed.

Using reasoners to detect errors

Fruit fly FBbt ‘tibia’ Human FMA ‘tibia’

UBERON: tibia

UBERON: bone

is_a

is_a

is_a

Vertebrata

Drosophila melanogaster

part_of

Homo sapiens

is_a

only_in_taxon

part_of

disjoint_with

✗

Resource Description Framework(RDF)

Knowledge and data represented in simple statements called “triples”

Engineers shouldn’t be allowed to name things

RDF

• Ontologies support semantic classification of data• RDF is the data standard for the semantic web• Extremely simple and flexible data model (not a file

format)• A W3C recommendation:

http://www.w3.org/TR/2004/REC-rdf-primer-20040210/

RDF• Relate “things” with simple statements:

triples• <subject> <predicate> <object>• <John> <hasPet> <Fido>• Everything has a global, unique

identifier: URI• Objects/values can be “things” or literal

values (text, number, date)

Linked Data PrinciplesUse URIs as names for things.

Use HTTP URIs so we can look up the names.

Return useful information using standards when someone looks up a name.

Link to other URIs so we can discover more things (“follow your nose”).

http://www.w3.org/DesignIssues/LinkedData.html

RDF data integration

Easy to combine separate datasets– Shared identifiers– Unordered collection of triples (facts)

• example:john example:hasPet example:fido• example:John rdfs:label “John”• example:fido rdfs:label “Fido”• example:john rdf:type example:Person

• example:fido fdf:type example:Dog• example:fido example:age “6”

Dataset 1

Dataset 2

RDF data integration

Combined dataset

• example:john example:hasPet example:fido• example:John rdfs:label “John”• example:fido rdfs:label “Fido”• example:john rdf:type example:Person• example:fido rdf:type example:Dog• example:fido example:age “6”

RDF data integration

An Introduction to OWL

What is OWL?

• Web Ontology Language (OWL) is a language for writing ontologies for the Web

• OWL 2 is the current version of OWL and a W3C recommendation as of October 2009

• The previous version of OWL (OWL 1) was a W3C recommendation in 2004

• An overview of the language can be found at: http://www.w3.org/TR/owl2-overview/

OWL

The things or objects about which knowledge is represented (e.g. Melissa, Jim, Sweden) are called individuals (aka instances)

Group of things (e.g. person, conference) are called classes

Relations between things (e.g. siblings) are called properties

What does OWL add to RDF? More meaningful semantics

– OWL allows one to specify characteristics of properties and classes• "If Melissa isMarriedTo John" then this implies “John

isMarriedTo Melissa” (symmetric property)• Complex class definitions composed of properties and

other classes—“expressions”– muscle and (attached_to some bone and (part_of some

head))

Enables reasoning

OWL - added semantics

Infer new RDF triples (facts) based on asserted triples and OWL axioms

OWL 2 Full - direct extension of RDF OWL 2 DL - practical reasoning based on

Description Logic– strict separation of classes and instances– entails some constraints on RDF

Class versus individual assertions

EquivalentTo, subClassOf, disjointWith

In OWL, the things you can say about relationships between classes are basically limited to:

All other relationships (aka properties) are between OWL individuals:

Built in properties: sameAs, differentFrom

Relationship between individual and class: Type

Object properties: user-added content in the ontology

OWL primer

http://www.w3.org/TR/owl2-primer/

This is an EXCELLENT resource

Object Properties

• Object properties link another individual to an individual• The above representations means that the individual Melissa and

INCV ontology lecture are related through the teaches property

Melissa

INCF Ontology Lecture

teaches

Object Properties

Properties may have a domain and a range specified. For instance, the property teaches can have domain Person and range Lecture

teaches rdf:type owl:ObjectProperty teaches rdfs:domain foaf:Personteaches rdfs:range Lecture

Melissa

INCF Ontology Lecture

LecturePerson

teaches

Complex Class definition (Equivalent Class)

In OWL, we can say that an instructor is equivalent to a person that teaches some workshop

PersonteachessomeLecture

instructor

Class: InstructorEquivalentTo: Person and (teaches some lecture)

Reasoning ExampleAssuming we have defined the object property teaches with domain foaf:person and range lectureWe assert thatMelissa teaches INCF_ontology_lecture

Melissa rdf:type person INCF_ontology_lecture rdf:type lecture

And because of the equivalent class, we also infer that: Melissa rdf:type instructor

Melissa

INCF Ontology Lecture

LecturePerson

teaches

Instructor

Reasoning ExampleAssuming we have defined the object property teaches with domain foaf:person and range lectureWe assert thatMelissa teaches INCF_ontology_lectureINCF_ontology_lecture rdf:type book

book owl:disjointWith lecture

??

Reasoning ExampleAssuming we have defined the object property teaches with domain foaf:person and range lectureWe assert thatmandy teaches INCF_ontology_lectureINCF_ontology_lecture rdf:type bookbook owl:disjointWith lecture

Error!!

Satisfiability• Unsatisfiable class: impossible to have

instances• Incoherent ontology: contains unsatisfiable

classes– still usable

• Inconsistent ontology: contains impossible instances (such as instances of unsatisfiable classes)– not really usable: “anything follows from a

contradiction”– http://ontogenesis.knowledgeblog.org/1329

Open World vs. Closed World

Open world assumption: everything we don’t know is undefined

Closed world assumption: everything we don’t know is false

Example:Statement: Vertebrate has_part spinal cordQuery: “Do fruit flies have spinal cords?”

Closed world response= noOpen world response= unknown

OWL Syntaxes• Many flavors

– Functional OWL Syntax– RDF based Syntaxes

• RDF/XML• Turtle• OWL/XML

• Manchester SyntaxStill they all can be represented as a graph or a set of triples!

So what can we do with all this ontology “stuff”?

Querying across anatomical granularity

CJ Mungall, C Torniai, GV Gkoutos, SE Lewis, MA Haendel. 2012. Uberon, an integrative multi-species anatomy ontology. Genome biology 13 (1), R5

Uberon.org

Top hit mouse

Top hit fish

Exomiser: Using comparative phenotype analyses to improve variant prioritization

Ontology considerations An ontology is a classification and there are many

useful ways to classify biology Everybody makes mistakes

Let the computer find errors, manage knowledge for you Re-use other people’s work where possible

Import class hierarchies, use common patterns Cautionary note – formal languages have limitations.

Don’t expect or want to express everything! RDF is a model for semantic, linked data OWL is an ontology language for RDF and the web Ontologies are a tool for data exchange and integration

via a variety of formats (tomorrow’s discussion)

A

C

B

D EVertebrata

Ascidians

Arthropoda

Annelida

Mollusca

Echinodermata

tetrapod limbs

ampullae

tube feet

parapodia

Querying for genes in similar structures across species

Panganiban et al., PNAS, 1997

Distal-less orthologs participate in distal-proximal pattern formation and appendage morphogenesis

mouse limbsea urchin tube feet

ascidian ampulla polychaete parapodia