Robert Stevens Ebi

8/8/2019 Robert Stevens Ebi

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Building and Using Ontologies

Robert StevensDepartment of Computer Science

University of Manchester

Manchester UK


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Introduction The nature of bioinformatics resources

W

hat is knowledge? What is an ontology?

What are the uses of ontologies?

Components of an ontology

Building an ontology (in brief)


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The Nature of Bioinformatics

Resources Over 500 databanks and analysis tools that work over

resources

Repositories of knowledge and data and generation ofnew knowledge

Knowledge often held as free text; some use made ofcontrolled vocabularies

Enormous amount of semantic heterogeneity and poorquery facilities

Knowledge about services not always apparent


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What is Knowledge?

Knowledge all informationand an understanding tocarry out tasks and to infernew information

Information -- data equippedwith meaning

Data -- un-interpretedsignals that reach oursenses

PATRICIAGRACEKENNEDY

SAIDMINEISAPINT

Patricia Grace

Kennedy said

mine is a pintname noun verb

Pat Baker is aManchester

bioinformatician

who drinks beer.

CEKENNSingle letter amino

acid codes

C cysteine

K - lysine

Protein that acts asa tyrosine kinase in

the liver of primates.


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Capturing Knowledge Capturing knowledge for both humans an computer

applications

A set of vocabulary definitions that capture acommunitys knowledge of a domain

`An ontology may take a variety of forms, butnecessarily it will include a vocabulary of terms, and

some specification of their meaning.T

his includesdefinitions and an indication of how concepts are inter-related which collectively impose a structure on thedomain and constrain the possible interpretations ofterms.'


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What Does an Ontology Do? Captures knowledge

Creates a shared understanding between

humans and for computers

Makes knowledge machine processable

Makes meaning explicit by definition and

context


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What is an Ontology?

Catalog/

ID

General

Logical

constraints

Terms/

glossary

Thesauri

narrower

term

relation Formal

is-a

Frames

(properties)

Informal

is-a

Formal

instance

Value Restrs. Disjointness,

Inverse, part

of


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Roles of Ontologies in

Bioinformatics We can divide ontology use into three types:

Domain-oriented, which are either domain specific (e.g.

E. coli) or domain generalisations (e.g. gene function orribosomes);

Task-oriented, which are either task specific (e.g.annotation analysis) or task generalisations (e.g.

problem solving); Generic, which capture common high level concepts,

such as Physical, Abstract and Substance. Important inontology management and language applications.


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Uses of Ontology Community reference -- neutral authoring.

Either defining database schema or defining a common

vocabulary for database annotation -- ontology asspecification.

Providing common access to information. Ontology-based search by forming queries over databases.

Understanding database annotation and technicalliterature.

Guiding and interpreting analyses and hypothesisgeneration


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Components of an Ontology Concepts: Class of individuals The concept

Protein and the individual`human cytochrome C

Relationships between concepts

Is a kind of relationship forms a taxonomy

Other relationships give further structure is a

part of

Axioms Disjointness, covering, equivalence,


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Knowledge Representation

Ontology are best delivered in some computablerepresentation

Variety of choices with different:

Expressiveness

The range of constructs that can be used to formally,

flexibly, explicitly and accurately describe the ontology

Ease of use

Computational complexity

Is the language computable in real time?

Rigour -- Satisfiability and consistency of the

representation

Systematic enforcement mechanisms

Unambiguous, clear and well defined semantics


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Languages Vocabularies using natural language

Hand crafted, flexible but difficult to evolve, maintain and

keep consistent, with weak semantics

Gene Ontology

Object-based KR: frames Extensively used, good structuring, intuitive. Semantics

defined byOKBC standard

EcoCyc (uses Ocelot) and RiboWeb (uses Ontolingua)

Logic-based: Description Logics Very expressive, model is a set of theories, well defined

semantics

Automatic derived classification taxonomies

Concepts are defined and primitive


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Building Ontologies No field ofOntologicalEngineering equivalent to

Knowledge or Software Engineering;

No standard methodologies for building ontologies;

Such a methodology would include:

a set of stages that occur when building ontologies;

guidelines and principles to assist in the different stages;

an ontology life-cycle which indicates the relationships

among stages.


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The Development Lifecycle Two kinds of complementary methodologies emerged:

Stage-based, e.g. TOVE[Uschold96]

Iterative evolving prototypes, e.g. MethOntology [Gomez Perez94].

Most have TWO stages:

1. Informal stage ontology is sketched out using either natural language descriptions or some

diagram technique

2. Formal stage

ontology is encoded in a formal knowledge representation language, that is

machine computable

the informal representation helps the former

the formal representation helps the latter.


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A Provisional Methodology

A skeletal methodology and life-cycle for buildingontologies;

Inspired by the software engineering V-process model;

The overall process moves through a life-cycle.

The left side

charts the

processes in

building anontology

The right side charts the

guidelines, principles and

evaluation used to quality

assure the ontology


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The V-model Methodology

Conceptualisation

Integrating existingontologies

Encoding

Representation

Identify purpose and scope

Knowledge acquisition

Evaluation: coverage,verification, granularity

ConceptualisationPrinciples: commitment,conciseness, clarity,extensibility, coherency

Encoding/Representationprinciples: encoding bias,consistency, house stylesand standards, reasoningsystem exploitation

Ontology in Use

User Model

Conceptualisation Model

Implementation Model


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The ontology building life-

cycleIdentify purpose and scope

Knowledge acquisition

Evaluation

Language andrepresentation

Availabledevelopmenttools

Conceptualisation

Integrating

existingontologiesEncoding

Building


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Starting Concept List Chemicals atom, ion, molecule, compound, element;

Molecular-compound, ionic-compound, ionic-molecular-

compound, ;

Ionic-macromolecular-compound and ionic-small-

macromolecular-compound;

Protein, peptide, polyprotein, enzyme, holoprotein,apoprotein,

Nucleic acid DNA, RNA, tRNA, mRna, snRNA,


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Conceptualisation SketchChemical

AtomElementCompoundMolecule Ion

MetalNon-Metal

Metaloid

Molecular

Compound

Molecular

Element

Ionic

Compound

Ionic

Molecule

Ionic Molecular

Compound


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Molecule Conceptualisation

Sketch

Nucleic

Acid

ProteinPolysaccharide

DNA RNAEnzyme

Macromolecule Small

Molecule

Ionic Macromolecular

Compound

Starch Glycogen

mRNA tRNA rRNAsnRNA

Peptide


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Initial Encodingclass-def chemical

subclass-of substance

class-def molecule

subclass-of chemical

class-def compound


class-def molecular-compound

subclass-of molecule and compound


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Molecules Revisited

Nucleic

Acid

ProteinPolysaccharide

DNA RNAEnzyme

Macromolecule Small

Molecule

Ionic Macromolecular

Compound

Starch Glycogen

mRNA tRNA rRNAsnRNA

Peptide

Non-Ionic Macromolecular

Compound


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More Encodingclass-def chemical

subclass-of substance

class-def defined molecule


Slot-constraint contains-bond min-cardinality 1 has-value covalent-bond

class-def defined compound


Slot-constraint has-atom-types greater-than 1

class-def defined molecular-compound

subclass-of molecule and compound


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Expansion Sketch and encode in cycles

Build a taxonomy of a small portion

Then build links to other portions

Add more detail

Document sources, author, date andargumentation.


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Summary An ontology captures knowledge for a shared

understanding

The important question is not whether an artefact is anontology, but whether it does any good

Making our understanding of domain explicit, consistent

and processable

Bioinformatics resources are knowledge resources

needs to be both human and machine understandable

Documents

Robert Stevens Ebi