Dave Thau PASI, Costa Rica, June 7, 2008 Ontologies in Ecology and Biodiversity Informatics Dave...

Dave ThauPASI, Costa Rica, June 7, 2008

Ontologies in Ecology and Biodiversity Informatics

Dave Thau

With some slides by Shawn Bowers and Josh Madin gratefully reused with permission


Four Chapters

I. What are ontologies and why should we care?

II. Some nitty gritty

III. Ontologies in ecology and biodiversity informatics

IV. Tools


Talk Goals• Learn about ontology successes

• Learn basic terminology / buzz words

• Get a sense for ontology development

• See how they apply to ecology and biodiversity

• Learn what remains to be done

– Bottom line: A LOT!


Ontology Defined

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are needed to see this picture.

Trapeziid Crab Pincer Acrophora Oceanlives inhas part

lives in


notebook

The Way It’s Been


The PlanHow are the finchesdoing these days?

1. Find data sets:“give me all data sets describing finch abundance”

Finches R’ UsWorld FinchDatabase

Finch FancyRepository

2. Find analysis:“find a way to plot theirdistribution”

Plotter Workflow

3. Integrate data,plug it in, get results


Where Ontologies Can Help

Finches R’ UsWorld FinchDatabase

Plotter Workflow

Finch FancyRepositoryFinding the right

Data sets

Integrating the data

Finding a good analysis and

making sure data fits the analysis Making the results

discoverable


Other Ways Ontologies Help

• Crystalize knowledge

• Lay open assumptions

• Makes for great parties


Simple AssemblySimple Assembly

Assembly With SwitchAssembly With Switch

Assembly-1Assembly-1

Instance Of

Subclass Of

Successes

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The GO Ontology: www.geneontology.org


Gene Ontology widely adopted

AgBase


GOOver 25,000 terms

19 Contributing groups

GO AnnotationsUniProtKB O13035 GO:0004098UniProtKB O13035 GO:0004336UniProtKB O13035 GO:0004348

Total manual GO annotations - 388,633

Total proteins with manual annotations – 80,402

Total number distinct proteins – 2,971,374

Total number taxa – 129,318

GO Stats

I


Ontologies and You

• User of “invisible” ontologies – like search

• User of created ontologies – annotating data sets

• Collaborator in ontology creation– biologist working with ontologist

• Hands-on ontology builder– you’ll need more than a 1 hour talk…


Chapter I Summary

• Ontologies can help– Locate data– Add semantics to data– Integrate data– Clarifiy domains

• There are already good examples– In genomics– In biomedical field– In engineering


The Nitty Gritty

• XML, RDF, OWL and other 3 letter words

• Ontology Basics

• Reasoning with Ontologies


XML, DTDs, XML Schema

Not good for machinestools can’t automatically processhow do you know it’s valid?


XML

XML, XML Schema

<?xml version='1.0'?>

<dataset>

<dataitem>

<col>hya</col>

<ht>1.5</ht>

<crabs>11</crabs>

</dataitem>

…

</dataset>

Col.,Ht.,Crabshya,1.5,11

XML Schema

string

float

integer


XML and XML Schema

• Now any machine can validate an XML document, given a schema

• Languages to translate XML to PDF or HTML exist

• But…. Can’t relate things– Like “the data in this file relates to study X”


The Resource Description Framework (RDF)– individuals (objects), properties, and classes

RDF and RDF Schema

livesIn

My Crab That Coral

A. CoralT.CrablivesIn

type type

Coral

subClassOf

Crab

subClassOf


RDF is Useful• GO is available in RDF

• FOAF - Friend of a Friend– For example, go to– http://xml.mfd-consult.dk/foaf/explorer/– Enter: http://hello.typepad.com/foaf.rdf

• RSS - Really Simple Syndication– It’s probably in your browser– Yahoo pipes rss blender

Person Personknows

David Jacobs

randomwalks.com

imgname

Jesse James Garrett

name

blog.jjg.net

homepagehomepage

<foaf:Person> <foaf:weblog rdf:resource="http://hello.typepad.com/" /> <foaf:homepage rdf:resource="http://www.randomwalks.com" /> <foaf:name>David Jacobs</foaf:name> <bio:olb>I work in New York City with filmmakers, activists and educators. </bio:olb> <foaf:img rdf:resource="http://hello.typepad.com/mirrorshot.jpg" /> <foaf:knows> <foaf:Person> <foaf:name>Jesse James Garrett</foaf:name> <foaf:homepage rdf:resource="http://blog.jjg.net/weblog/" /> <rdfs:seeAlso rdf:resource="http://blog.jjg.net/foaf.rdf" /> </foaf:Person> </foaf:knows></foaf:Person>

FOAF

seeAlso


Basic Ontology Building BlocksInstances

– The actual things of interest

– For example, a specimen (that crab)

Classes (concepts)

– A set of instances that share certain characteristics

– For example, the set of all crabs

is-a

– A is-a B means every instance of A is also an instance of B

– A might have additional characteristics; more restrictions

Properties (has-a / part-of)

– Represent a characteristic

– e.g., has Wings, has-color Yellow

crab

isa

crab

T.crabhas-colorcrab color

The crab that bit me


Example of Pollution Ontology


Classes versus Instances - tricky!

– If A is-a B, then every A is B

– Every human, in this case, must also be a species

– But “John” is not a species

Species

Human

John

is-a

instance

Species

Human

John

Species Human

JohnHuman

(Guarino)


is-a is not part-of

– What are essential properties of Cars?

• E.g., that they accommodate people?

– Are these also essential for Engines?

Car

Engine

part-of

Car

Engine

[Guarino]

Wheel

part-of

EngineCar

part-of


Limitations of RDF-based Ontologies

• No constraints - – “all red things have the color property with value red”– “Costa Rica has only one President”

• Can’t create definitions by combining other definitions– Mother = Parent and Female

• Can’t say concepts are equivalent or disjoint


OWL - The Web Ontology Language

• Three different kinds– Lite - limited, but still powerful– DL - very expressive, can still reason– Full - extremely expressive, but unreasonable

• Example Reasoning OWL– If all apples are red, and apples and

manzanas are the same, then all manzanas are red


Reasoning about Taxonomy

Peet’s 2005Ranunculusdata set:9 Taxonomies654 Taxa704 Relations

visualization byMartin Graham


Is This Right?

Peet, 2005: B.1948:R.h.stolonifer is congruent to K.2004:R.h.stoloniferB.1948:R.h.typicus is congruent to K.2004:R.h.typicusB.1948:R. hydrocharoides is congruent to K.2004:R. hydrocharoides

The most likely fix here is to change the congruence relation between the toptwo nodes to instead state that Benson's R. hydrocharoides includesKartesz's

Ranunculushydrocharoides


R.h. varnatans

R.h. varnatans

R.h. varstolonifer

R.h. varstolonifer

R.h. vartypicus

R.h. vartypicus



R.h. varstolonifer

R.h. varstolonifer

R.h. vartypicus

R.h. vartypicus

Assuming disjoint children and complete partitioning of parents

⊋Benson, 1948 Kartesz, 2004


Getting Crazy with Properties• Properties can be:

– Transitive (a is inCountry b, b is inCountry c..) – Inverse (a partOf b, b has_part a)– Functional (dave’s birthMother is vera)– Inverse functional (dave’s ssn is ….)

• And you can say stuff like– Apples are only red– Some apples are red– Crabs have 2 pincers


Chapter II Summary

• XML is about syntax• RDF is about relationships• OWL is about more complex constraints• Tips:

– If A is-a B, then every instance of A is also an instance of B

– Keep classes and instances separate– is-a is not part-of


Chapter III: Ontologies in Ecology

• GO and friends are successful but..

• Hard to represent processes– Show me studies about the flow of nitrogen in highly

saline lakes, starting with lake-side nitrate

• Can’t be used for data integration

• Ecologists use complex models that involve many relations beyond is-a and part of relations


Reminder:Where Ontology Can Help

• Crystalizing domain knowledge

• Marking up metadata and data sets

• Marking up analyses, and analysis components


Marking Up Metadata and Data

Taxonomic Working Group Standards

http://rs.tdwg.org/ontology/voc/

Geo.owlSpecies.owlVegetation.owlGeography.owlWater.owlEcosystem.owl

Alternethttp://www5.umweltbundesamt.at/ALTERNet


Metadata and Data with OBOE

Example data set: the abundance of Trapeziid crabs in coral colonies (Stewart et al. 2006)







Two measurements of the organism: the name …



: Organism

ofEntity

: Observation : MeasurementhasMeasurement

: TaxonNameofCharacteristic

: TaxonCatalog

usesStandard

“Acroporahyacinthus”

hasValue


Two measurements of the organism: the name … height



: Organism

ofEntity



: TaxonCatalog

usesStandard


hasValue

: Measurement : HeightofCharacteristic

: Meter

usesStandard

“1.25”

hasValue

“0.01”

hasPrecision

hasMeasurement





: Organism

ofEntity



: TaxonCatalog

usesStandard


hasValue

: Measurement : HeightofCharacteristic

: Meter

usesStandard

“1.25”

hasValue

“0.01”

hasPrecision

hasMeasurement



: Observation

hasContext

: Measurement : TaxonNameofCharacteristic

: TaxonCatalog

usesStandard

“Trapeziidcrab”

hasValue

: Measurement : AbundanceofCharacteristic

: Individual

usesStandard

“11”

hasValue

hasMeasurement

hasMeasurement



: Coral

ofEntity

: ObservationhasMeasurement

: Measurement : DiameterofCharacteristic

: Meter

usesStandard

“1.25”

hasValue

“0.01”

hasPrecision

(a)

: Animal

ofEntity


: Measurement : ColonyDiamaterofCharacteristic

: Centimeter

usesStandard

“320”

hasValue

“10”

hasPrecision

(b)

Integration of data sets given their observation semantics

Data Integration with OBOE


: Coral

ofEntity



: Meter

usesStandard

“1.25”

hasValue

“0.01”

hasPrecision

(a)

: Animal

ofEntity


: Measurement : ColonyDiamaterofCharacteristic

: Centimeter

usesStandard

“320”

hasValue

“10”

hasPrecision

(b)

Integration involves data set observation structures

is-a is-a: Length

hasDimension

hasDimension



: Animal

ofEntity



: Meter

usesStandard

“1.3”

hasValue

“0.1”

hasPrecision

(c)

And then applying appropriate conversions, etc.

(a)

“3.2” (b)



Marking up Analyses

• Scientific Workflow Systems help:– Make analyses reproducible– Make parts of analyses reusable

• But…– 100’s of workflows and templates– 1000’s of actors (e.g. actors for web

services, data analytics, …)

• Need to find what you want


Semantic Type Annotation in Kepler

Component input and output port annotationEach port can be annotated with multiple terms from multiple ontologiesAnnotations are stored within the actor metadata


Chapter III Summary

• Taxonomies and partonomies are useful but limiting

• We saw a couple of ontologies for– Representing a domain– Describing data

• Again, the focus is always on discovery, integration and reuse


Tools• For RDF:

– Simile : simile.mit.edu - nice RDF tools

• For OWL:– Protégé : protege.stanford.edu

• For reasoning:– Pellet: http://www.mindswap.org/2003/pellet/– Jena: http://jena.sourceforge.net/inference/


Protégé


OWLViz Tab


Summing Up

• Ontologies are useful for– Data discovery– Data integration– Terminology regulation– Analysis Reuse

• Ontology in ecology and biodiversity is just getting started


Lastly: Back to the Goals• Learn about ontology successes

• Learn basic terminology / buzz words

• Get a sense for ontology development

• See how and where they apply to ecology

and biodiversity studies

• Learn what remains to be done

– Bottom line: A LOT!


Some ReferencesPractical guides/references

– Protégé. Open source ontology editor. http://protege.stanford.edu/ – CO-ODE. Various resources on ontologies, tutorials, best-practices, etc. http://www.co-ode.org/– W3C Semantic Web Activity. Various pointers, standardization efforts, etc.

http://www.w3.org/2001/sw/ – OWL Resources: OWL-Guide (http://www.w3.org/TR/owl-guide/), OWL-Reference (

http://www.w3.org/TR/owl-ref/)– Pizza Tutorials. http://www.co-ode.org/resources/tutorials/

Academic Papers/Collections– Bard and Rhee. Ontologies in biology: Design, applications and future challenges. Nature

Reviews, Genetics, vol. 5, 2004.– The Gene Ontology Consortium. Gene Ontology: tool for the unification of biology. Nature Genet.

25: 25-29, 2000– Barry Smith, http://ontology.buffalo.edu/smith/, various papers on ontologies (even for ecology)– Sowa, J. F. Knowledge Representation: Logical, Philosophical, and Computational Foundations.

PWS Publishing Co., Boston, 1999.– Baader F., Calvanese D., McGuinness D., Nardi D., and Patel-Schneider P. The Description

Logic Handbook: Theory, Implementation, and Applications. Cambridge Univ. Press, 2003.– Thomas R. Gruber. Toward principles for the design of ontologies used for knowledge sharing. In

Formal Ontology in Conceptual Analysis and Knowledge Representation, Kluwer Academic Publishers, 1993.

– Nicola Guarino. Formal ontology and information systems. In Proc. of Formal Ontology in Information Systems, IOS Press, pp. 3-15, 1998.

http://protege.stanford.edu/

http://www.co-ode.org/

http://www.w3.org/2001/sw/

http://www.w3.org/TR/owl-guide/

http://www.w3.org/TR/owl-ref/

http://www.co-ode.org/resources/tutorials/

http://ontology.buffalo.edu/smith/


Exercise: Ontology Engineering1. Choose the specific “domain” you want to tackle:

• Based on a specific collection of data that you are familiar with• Based on an existing project/experiment you are working on or

understand• Focus on use: data set markup or describing a domain

2. Define (a part of) an ontology for the domain• Start with the classes• Then arrange into an isa hierarchy• Then add properties between the classes• If you feel mighty, try some property constraints

3. Capture your ontology on whiteboard, poster board, or cmap tool as one or more diagram Transitive

InverseFunctionalInverse FunctionalAll Apples have a colorSome Apples have a colorAll apples are redSome apples are redCrabs have 2 pincers

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