ontology and the semantic web barry smith august 26, 2013 1

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Ontology and the Semantic Web

Barry Smith

August 26, 2013

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Ontologies

• are computer-tractable representations of types in specific areas of reality

• are more and less general (upper and lower ontologies)– upper = organizing ontologies– lower = domain ontologies

3FMA

Pleural Cavity

Pleural Cavity

Interlobar recess

Interlobar recess

Mesothelium of Pleura

Mesothelium of Pleura

Pleura(Wall of Sac)

Pleura(Wall of Sac)

VisceralPleura

VisceralPleura

Pleural SacPleural Sac

Parietal Pleura

Parietal Pleura

Anatomical SpaceAnatomical Space

OrganCavityOrganCavity

Serous SacCavity

Serous SacCavity

AnatomicalStructure

AnatomicalStructure

OrganOrgan

Serous SacSerous Sac

MediastinalPleura

MediastinalPleura

TissueTissue

Organ PartOrgan Part

Organ Subdivision

Organ Subdivision

Organ Component

Organ Component

Organ CavitySubdivision

Organ CavitySubdivision

Serous SacCavity

Subdivision

Serous SacCavity

Subdivision

part_

of

is_a

Foundational Model of Anatomy

ontologies = standardized labels designed for use in annotations

to make the data cognitively accessible to human beings

and algorithmically accessible to computers

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by allowing grouping of annotations

brain 20 hindbrain 15 rhombomere 10

Query brain without ontology 20Query brain with ontology 45

Ontologies facilitate retrieval of data

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ontologies = high quality controlled structured vocabularies used for the annotation (description, tagging) of data, images, emails, documents, …

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Ontology’s greatest successes around net-centricity

• You build a site• Others discover the site and they link to it• The more they link, the more well known the

page becomes (Google …)• Your data becomes discoverable• Your data becomes more easily discoverable

the more you use common vocabularies

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1. Each group creates a controlled vocabulary of the terms commonly used in its domain, and creates an ontology out of these terms using OWL (Web Ontology Language) syntax

4. Binds this ontology to its data and makes these data available on the Web

5. The ontologies are linked e.g. through their use of some common terms

6. These links create links among all the datasets, thereby creating a ‘web of data’

7. We can all share the same tags – they are called internet addresses

The roots of Semantic Technology

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Audio Features Ontology

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Audio Features Ontology

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Where we stand today• increasing availability of semantically enhanced

data and semantic software• increasing use of OWL (Web Ontology Language)

in attempts to create useful integration of on-line data and information

• “Linked Open Data” the New Big Thing

12as of September 2010

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The problem: the more this sort of Semantic Technology is successful, they more it fails

The original idea was to break down silos via common controlled vocabularies for the tagging of data

The very success of the approach leads to the creation of ever new controlled vocabularies – semantic silos – as ever more ontologies are created in ad hoc ways

Every organization and sub-organization now wants to have its own “ontology”

The Semantic Web framework as currently conceived and governed by the W3C yields minimal standardization

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Divided we fail

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United we also fail

The problem: many, many silos

• DoD spends more than $6B annually developing a portfolio of more than 2,000 business systems and Web services

• these systems are poorly integrated• deliver redundant capabilities, • make data hard to access, foster error and waste• prevent secondary uses of data

https://ditpr.dod.mil/ Based on FY11 Defense Information Technology Repository (DITPR) data

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https://ditpr.dod.mil/

https://ditpr.dod.mil/

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what is missing here

Syntactic and semantic interoperability

• Syntactic interoperability = systems can exchange messages (realized by XML).

• Semantic interoperability = messages are interpreted in the same way by senders and receivers.

• In UCore, meanings are specified via natural-language strings.

• Experience shows that this is not a viable route to achieving semantic interoperability.

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How to avoid the problem of semantic siloes

Distributed Development of a Shared Semantic Resource

Pilot testing to demonstrate feasibility for I2WD

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An alternative solution: Semantic Enhancement

A distributed incremental strategy of coordinated annotation

– data remain in their original state (is treated at ‘arms length’)– ‘tagged’ using interoperable ontologies created in tandem– allows flexible response to new needs, adjustable in real

time– can be as complete as needed, lossless, long-lasting because

flexible and responsive– big bang for buck – measurable benefit even from first small

investments

The strategy works only to the degree that it rests on shared governance and training

compare: legends for mapscompare: legends for maps

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compare: legends for mapscommon legends allow (cross-border) integration

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The Gene Ontology

MouseEcotope GlyProt

DiabetInGene

GluChem

sphingolipid transporter

activity

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The Gene Ontology


DiabetInGene

GluChem

Holliday junction helicase complex

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The Gene Ontology


DiabetInGene

GluChem

sphingolipid transporter

activity

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Common legends• help human beings use and understand complex

representations of reality• help human beings create useful complex

representations of reality• help computers process complex

representations of reality• help glue data together

But common legends serve these purposes only if the legends are developed in a coordinated, non-redundant fashion

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International System of Units

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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 Compone

nt(FMA, GO)

Cellular Function

(GO)

MOLECULEMolecule

(ChEBI, SO,RnaO, PrO)

Molecular Function(GO)

Molecular Process

(GO)The Open Biomedical Ontologies (OBO) Foundry

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ORGAN ANDORGANISM

Organism(NCBI

Taxonomy)

Anatomical Entity

(FMA, CARO)

OrganFunction


Quality(PaTO)

Organism-Level Process

(GO)

CELL AND CELLULAR

COMPONENT

Cell(CL)

Cellular Compone

nt(FMA, GO)

Cellular Function

(GO)

Cellular Process

(GO)

MOLECULEMolecule

(ChEBI, SO,RNAO, PRO)


Molecular Process

(GO)

rationale of OBO Foundry coverage

GRANULARITY

RELATION TO TIME

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RELATION TO TIME

GRANULARITY



COMPLEX OFORGANISMS

Family, Community, Deme, Population

OrganFunction

(FMP, CPRO)

Population Phenotype

PopulationProcess

ORGAN ANDORGANISM

Organism(NCBI

Taxonomy)

Anatomical Entity(FMA, CARO) Phenotypic

Quality(PaTO)

Biological Process

(GO)CELL AND CELLULAR

COMPONENT

Cell(CL)

Cellular Componen

t(FMA, GO)

Cellular Function

(GO)

MOLECULEMolecule



Molecular Process

(GO)Population-level ontologies 30

RELATION TO TIME

GRANULARITY



ORGAN ANDORGANISM

Organism(NCBI

Taxonomy)

Anatomical Entity(FMA, CARO)

OrganFunction


Quality(PaTO)

Biological Process

(GO)

CELL AND CELLULAR

COMPONENT

Cell(CL)

Cellular Compone

nt(FMA, GO)

Cellular Function

(GO)

MOLECULEMolecule



Molecular Process

(GO)Environment Ontology

envi

ron

men

ts

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RELATION TO TIME

GRANULARITY



COMPLEX OF ORGANISMS

Family, Community,

Deme, Population OrganFunction

(FMP, CPRO)

Population

Phenotype

Population Process

ORGAN ANDORGANISM

Organism(NCBI

Taxonomy)

(FMA, CARO)

Phenotypic Quality(PaTO)

Biological Process

(GO)

CELL AND CELLULAR

COMPONENT

Cell(CL)

Cell Com-

ponent(FMA, GO)

Cellular Function

(GO)

MOLECULEMolecule



Molecular Process

(GO)

http://obofoundry.org

E N

V I R

O N

M E

N T

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RELATION TO TIME

GRANULARITY

CONTINUANT

INDEPENDENT

COMPLEX OF ORGANISMS

Family, Community,

Deme, Population

Environment of population

ORGAN ANDORGANISM

Organism(NCBI

Taxonomy)

(FMA, CARO)

Environment of single organism

CELL AND CELLULAR

COMPONENT

Cell(CL)

Cell Com-

ponent(FMA, GO)

Environment of cell

MOLECULEMolecule


Molecular environment

http://obofoundry.org

E N

V I R

O N

M E

N T

The OBO Foundry based on the idea of annotation = semantic enhancement of data across all of biology

$200 mill. spent so far on using the GO to annotate (tag) biomedical research data through manual effort of PhD biologusts

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OBO Foundry approach extended into other domains

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NIF Standard Neuroscience Information Framework

ISF Ontologies Integrated Semantic Framework

OGMS and Extensions Ontology for General Medical Science

IDO Consortium Infectious Disease Ontology

cROP Common Reference Ontologies for Plants

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What these annotations do

• make data retrievable even by those not involved in their creation

• allow integration of data deriving from heterogeneous sources

• break down the walls of roach motels

Benefits of the Approach• Does not interfere with the source content• Enables content to evolve in a cumulative fashion

as it accommodates new kinds of data• Does not depend on the data resources and can

be developed independently from them in an incremental and distributed fashion

• Provides a more consistent, homogeneous, and well-articulated presentation of the content which originates in multiple internally inconsistent and heterogeneous systems

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Benefits of the Approach• Makes management and exploitation of the

content more cost-effective• Allows graceful integration with other

government initiatives and brings the system closer to the federally mandated net-centric data strategy

• Creates incrementally an integrated content that is effectively searchable and that provides content to which more powerful analytics can be applied

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Building the Shared Semantic Resource

• Methodology of distributed incremental development

• Training• Governance• Common Architecture of Ontologies to support

consistency, non-redundancy, modularity– Upper Level Ontology (BFO)– Mid-Level Ontologies– Low Level Ontologies

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Goal: To realize Horizontal Integration(HI) of intelligence data

HI =Def. the ability to exploit multiple data sources as if they are one Problem: the data coming onstream are out of our

control Any strategy for HI must be agile in the sense that

it can be quickly extended to new zones of emerging data according to need

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I2WD StrategyCreate an agile strategy for building ontologies within a Shared Semantic Resource (SSR)

and apply and extend these ontologies to annotate new source data as they come onstream

⁻ Problem: Given the immense and growing variety of data sources, the development methodology must be applied by multiple different groups

⁻ How to manage collaboration?

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Why do large-scale ontology projects fail?

• focus on vocabularies, lexicons, with no logical structure, no attention to life cycle

• failure of housekeeping yields redundancy and therefore forking

• the same data is annotated in different ways by users of different ontology fragments

• data is siloed as before– HOW TO BUILD THE NEEDED LOGIC INTO THE

ARCHITECTURE OF THE ONTOLOGIES?42

Examples of Principles• All terms in all ontologies should be singular nouns• Same relations between terms should be reused in

every ontology• Reference ontologies should be based on single

inheritance• All definitions should be of the form

an S = Def. a G which Dswhere ‘G’ (for: genus) is the parent term of S (for: species) in the corresponding reference ontology

Anatomy Ontology(FMA*, CARO)

Environment

Ontology(EnvO)

Infectious Disease

Ontology(IDO*)

Biological Process

Ontology (GO*)

Cell Ontology

(CL)

CellularComponentOntology

(FMA*, GO*) Phenotypic Quality

Ontology(PaTO)

Subcellular Anatomy Ontology (SAO)

Sequence Ontology (SO*) Molecular

Function(GO*)Protein Ontology

(PRO*) Extension Strategy + Modular Organization 44

top level

mid-level

domain level

Information Artifact Ontology

(IAO)

Ontology for Biomedical

Investigations(OBI)

Spatial Ontology

(BSPO)

Basic Formal Ontology (BFO)

Ontologies are built as orthogonal modules which form an incrementally evolving network

• scientists are motivated to commit to developing ontologies because they will need in their own work ontologies that fit into this network

• users are motivated by the assurance that the ontologies they turn to are maintained by experts

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More benefits of orthogonality

• helps those new to ontology to find what they need

• to find models of good practice• ensures mutual consistency of ontologies

(trivially)• and thereby ensures additivity of annotations

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More benefits of orthogonality• No need to reinvent the wheel for each new

domain• Can profit from storehouse of lessons learned• Can more easily reuse what is made by others• Can more easily reuse training• Can more easily inspect and criticize results of

others’ work• Leads to innovations (e.g. Mireot, Ontofox) in

strategies for combining ontologies

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ontology and the semantic web barry smith august 26, 2013 1

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