The Semantic Web:New-style data-integration

(and how it works for life-scientists too!)

Frank van HarmelenAI Department

Vrije Universiteit Amsterdam

What’s the problem?

(data-mess in bio-inf)

The Study of Genes...

• Chromosomal location

• Sequence

• Sequence Variation

• Splicing

• Protein Sequence• Protein Structure

… and Their Function

• Homology

• Motifs

• Publications

• Expression

• HTS

• In Vivo/Vitro Functional Characterization

Understanding Mechanisms of Disease

Metabolic and

regulatory pathway induction

Development of Drugs, Vaccines, Diagnostics

Differing types of Drugs, Vaccines, and Diagnostics• Small molecules• Protein therapeutics• Gene therapy• In vitro, In vivo diagnostics

Development requires• Preclinical research• Clinical trials• Long-term clinical research

All of which often feeds back into ongoing Genomics research and discovery.

Sample Problem: Hyperprolactinemia

Over production of prolactin– prolactin stimulates mammary gland

development and milk production

Hyperprolactinemia is characterized by:– inappropriate milk production– disruption of menstrual cycle– can lead to conception difficulty

Understanding transcription factors for prolactin production

“Show me all genes in the public literature that are putatively related to hyperprolactinemia, have more than 3-fold expression differential between hyperprolactinemic and normal pituitary cells, and are homologous to known transcription factors.”

“Show me all genes that are homologous to known transcription factors”

SEQUENCE

1Q“Show me all genes that have more than 3-fold expression differential between hyperprolactinemic and normal pituitary cells”EXPRESSION

2Q

“Show me all genes in the public literature that are putatively related to hyperprolactinemia”

LITERATURE

3Q

(Q1Q2Q3)

The Industry’s Problem

Too much unintegrated data:– from a variety of incompatible sources

– no standard naming convention

– each with a custom browsing and querying mechanism (no common interface)

– and poor interaction with other data sources

ESTC Sept, 2008

Andy Law’s First Law

“The first step in developing a new genetic analysis algorithm is to decide how to make the input data file format different from all pre-existing analysis data file formats.”

ESTC Sept, 2008

ESTC Sept, 2008

Andy Law’s Second Law

“The second step in developing a new genetic analysis algorithm is to decide how to make the output data file format incompatible with all pre-existing analysis data file input formats.”

ESTC Sept, 2008

What are the Data Sources?

• Flat Files• URLs• Proprietary Databases• Public Databases• Data Marts• Spreadsheets• Emails• …

Stitching this all together by hand?

Source: Stephens et al. J Web Semantics 2006

Why would Semantic Web

technology help?

Semantic Web Approach1. Convert all data sources to

RDF representation (local or distributed)2. Optional: Collect the data to scalable

semantic repository3. Apply light-weight reasoning to specify

formal interpretations of the data, e.g.: remove redundancy, establish equalities, etc

4. Derive new implicit knowledge

ESTC Sept, 2008

machine accessible meaning (What it’s like to be a machine)

<name>

<symptoms>

<drug>

<drugadministration>

<disease>

<treatment>

IS-A

alleviatesMETA-DATA

What is meta-data?

it's just datait's data describing other dataits' meant for machine consumption

disease

name

symptoms

drug

administration

Required are:1. one or more standard vocabularies

so search engines, producers and consumersall speak the same language

2. a standard syntax, so meta-data can be recognised as such

3. lots of resources with meta-data attached

mechanisms for attribution and trust

no shared understanding

Conceptual and terminological confusion

Actors: both humans and machines

Agree on a conceptualization

Make it explicit in some language.

world

concept

language

What are ontologies &what are they used for

standard vocabularies (“Ontologies”)Identify the key concepts in a domainIdentify a vocabulary for these

conceptsIdentify relations between these

conceptsMake these precise enough

so that they can be shared between humans and humans humans and machines machines and machines

Real life examples handcrafted

music: CDnow (2410/5), MusicMoz (1073/7) biomedical: SNOMED (200k), GO (15k),

Emtree(45k+190kSystems biology

ranging from lightweight Yahoo, UNSPC, Open directory (400k)

to heavyweight (Cyc (300k))

ranging from small (METAR) to large (UNSPC)

Biomedical ontologies (a few..) Mesh

Medical Subject Headings, National Library of Medicine 22.000 descriptions

EMTREE Commercial Elsevier, Drugs and diseases 45.000 terms, 190.000 synonyms

UMLS Integrates 100 different vocabularies

SNOMED 200.000 concepts, College of American Pathologists

Gene Ontology 15.000 terms in molecular biology

NCBI Cancer Ontology: 17,000 classes (about 1M definitions),

Remember “required are”: one or more standard vocabularies

so search engines, producers and consumersall speak the same language

2. a standard syntax, so meta-data can be recognised as such

3. lots of resources with meta-data attached

Stack of languages

Bluffer’s guide to RDF (1)Object --Attribute-> Value triples

objects are web-resourcesValue is again an Object:

triples can be linked data-model = graph

pers05 ISBN...Author-of

pers05 ISBN...Author-of

MIT

ISBN...

Publ-by

Author-of Publ-

by

What does RDF Schema add?

• Defines vocabulary for RDF• Organizes this vocabulary in a

typed hierarchy• Class, subClassOf, type• Property, subPropertyOf• domain, range

Person

Teacher Student

subClassOfsubClassOf

Marta

type

supervisesdomain range

Frank

type

supervises

RDF Triples in Life Sciences

OWL: things RDF Schema can’t doequalityenumerationnumber restrictions

Single-valued/multi-valued Optional/required values

inverse, symmetric, transitiveboolean algebra

Union, complement…

Web of Data: anybody can say anything about anythingAll identifiers are URL's (= on the

Web) Allows total decoupling of

• data• vocabulary • meta-data

x T

[<x> IsOfType <T>]

differentowners & locations

<prince>

RDF(S) have a (very small) formal semanticsDefines what other statements are

implied by a given set of RDF(S) statements

Ensures mutual agreement on minimal contentbetween parties without further contact

In the form of “entailment rules”Very simple to compute

(and not explosive in practice)

RDF(S) semantics: examplesAspirin isOfType Painkiller

Painkiller subClassOf Drug Aspirin isOfType Drug

aspirin alleviates headachealleviates range symptom headache isOfType symptom

RDF(S) semantics: examplessp isOfType

subClassOf sp isOfType

sptstts range symptom isOfType symptom

RDF(S) semanticsX R Y + R domain T X IsOfType TX R Y + R range T Y IsOfType TT1 SubClassOf T2 +

T2 SubClassOf T3 T1 SubClassOf T3

X IsOfType T1 +T1 SubClassOf T2 X IsOfType T1

OWL also has a formal semantics

Defines what other statements are implied by a given set of statements

Ensures mutual agreement on content(both minimal and maximal)between parties without further contact

Can be used for integrity/consistency checking

Hard to compute (and rarely/sometime/always explosive in practice)

OWL semantics: minimalvanGogh isOfType Impressionist

Impressionist subClassOf Painter vanGogh isOfType Painter

vanGogh painter-of sunflowerspainter-of domain painter vanGogh isOfType painter

OWL semantics: maximalvanGogh isOfType Impressionist

Impressionist disjointFrom Cubist NOT: vanGogh isOfType Cubist

painted-by has-cardinality 1sun-flowers painted-by vanGoghPicasso different-individual-from vanGogh NOT: sun-flowers painted-by Picasso

Remember “required are”: one or more standard vocabularies

so search engines, producers and consumersall speak the same language

a standard syntax, so meta-data can be recognised as such

3. lots of resources with meta-data attached

Question: who writes the ontologies?Professional bodies, scientific

communities, companies, publishers, ….

See previous slide on Biomedical ontologies Same developments in many other fields

Good old fashioned Knowledge Engineering

Convert from DB-schema, UML, etc.

Question:Who writes the meta-data ?

- Automated learning- shallow natural language analysis- Concept extraction

amsterdam

trade

antwerp europe

amsterdam

merchant

city town

center

netherlandsmerchant

city town

Example: Encyclopedia Britannica on “Amsterdam”

Remember “required are” one or more standard vocabularies

so search engines, producers and consumersall speak the same language

a standard syntax, so meta-data can be recognised as such

lots of resources with meta-data attached

How to handle multiple ontologies: ontology matching

Linguistics & structure

Shared vocabulary

Instance-based matching

Shared background knowledge

Q

Matching through shared vocabulary

Matching through shared instances

sharedbackgroundknowledge

Matching using shared background knowledge

ontology 1 ontology 2

Some working examples?

• Linked Life Data http://www.linkedlifedata.com

• DOPE• HCLS http://www.w3.org/2001/sw/hcls/

ESTC Sept, 2008

Linked Life Data Overview

• LinkedLifeData - statistics:– Number of statements: 1,159,857,602 – Number of explicit statements: 403,361,589 – Number of entities: 128,948,564

• Platform to automate the process:– Infrastructure to store and inferences – Transform the structured data sources to RDF– Provide web interface to access the data

• Currently operates over OWLIM semantic repository

• Publicly available at: http://www.linkedlifedata.com

ESTC Sept, 2008

ESTC Sept, 2008

Light Weight Reasoning in Linked Life Data

ESTC Sept, 2008

rdf:type

rdf:type rdf:seeAlso

rdf:seeAlso

urn:intact:1007urn:uniprot:P104172

urn:uniprot:Protein urn:biogrid:Interaction

urn:biogrid:15904

urn:biogrid:FBgn00134235

urn:biogrid:FBgn0068575

urn:pubmed:15904

urn:uniprot:FBgn0068575

urn:uniprot:FBgn00134235

rdf:type

urn:intact:Interactionurn:uniprot:Q709356

interactsWith

interactsWith

hasParticipant

hasParticipant

rdf:typesameAs

sameAs

sameAs

Resolve the syntactic differences in the identifiersUse relationships to derive new implicit knowledge

These are only examples resource names

ESTC Sept, 2008ESTC Sept, 2008

Database Dataset Schema Description

Uniprot Curated entries

Original by the provider Protein sequences and annotations

Entrez-Gene Complete Custom RDF schema Genes and annotation

iProClass Complete Custom RDF schema Protein cross-references

Gene Ontology Complete Schema by the provider Gene and gene product annotation thesaurus

BioGRID Complete BioPAX 2.0 (custom generated) Protein interactions extracted from the literature

NCI - Pathway Interaction Database

Complete BioPAX 2.0 (original by the provider)

Human pathway interaction database

The Cancer Cell Map Complete BioPAX 2.0 (original by the provider)

Cancer pathways database

Reactome Complete BioPAX 2.0 (original by the provider)

Human pathways and interactions

BioCarta Complete BioPAX 2.0 (original by the provider)

Pathway database

KEGG Complete BioPAX 1.0 (original by the provider)

Molecular Interaction

BioCyc Complete BioPAX 1.0 (original by the provider)

Pathway database

NCBI Taxonomy Complete Custom RDF schema Organisms

Some working examples?

• Linked Life Data http://www.linkedlifedata.com

• DOPE• HCLS http://www.w3.org/2001/sw/hcls/

The Data Document repositories:

ScienceDirect: approx. 500.000 fulltext articles

MEDLINE: approx. 10.000.000 abstracts

Extracted Metadata The Collexis Metadata Server: concept-

extraction ("semantic fingerprinting")

Thesauri and Ontologies EMTREE:

60.000 preferred terms 200.000 synonyms

Summarising… Data integration on the Web:

machine processable data besides human processable data

Syntax for meta-data (not discussed in any detail)

Vocabularies for meta-data Lot’s of them in bio-inf.

Actual meta-data: Lot’s in bio-inf.

Will enable: Better search engines (recall, precision,

concepts) Combining information across pages (inference) …

Things to do for you Practical:

Use existing software to construct new use-scenario’s

Conceptual:Create on ontology for some area of bio-medical expertise

from scratch as a refinement of an existing ontology

Technical:Transform an existing data-set in meta-data format, and provide a query interface (for humans and machines)