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The Semantic Web: New-style data-integration (and how it works for life-scientists too!). Frank van Harmelen AI Department Vrije Universiteit Amsterdam. What’s the problem? (data-mess in bio-inf). Kenneth Griffiths and Richard Resnick Tut. At Intell. Systems for Molec. Biol., 2003. - PowerPoint PPT Presentation
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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)
Life Science Data
Recent focus on genetic data“genomics: the study of genes and their function. Recent advances in genomics are bringing about a revolution in our understanding of the molecular mechanisms of disease, including the complex interplay of genetic and environmental factors. Genomics is also stimulating the discovery of breakthrough healthcare products by revealing thousands of new biological targets for the development of drugs, and by giving scientists innovative ways to design new drugs, vaccines and DNA diagnostics. Genomics-based therapeutics include "traditional" small chemical drugs, protein drugs, and potentially gene therapy.”
The Pharmaceutical Research and Manufacturers of America - http://www.phrma.org/genomics/lexicon/g.html
Study of genes and their function
Understanding molecular mechanisms of disease
Development of drugs, vaccines, and diagnostics
Kenneth Griffiths and Richard ResnickTut. At Intell. Systems for Molec. Biol., 2003
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.
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
What are the Data Sources?
• Flat Files• URLs• Proprietary Databases• Public Databases• Data Marts• Spreadsheets• Emails• …
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 Complexity of Biological Data
Source: PhRMA & FDA 2003
Pharmaceutical Productivity
Stitching this all together by hand?
Source: Stephens et al. J Web Semantics 2006
The Medical tower of Babel 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
NCI Cancer Ontology: 17,000 classes (about 1M definitions),
Problem with the Current WWW
Why would Semantic Web
technology help?
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
is this page really about Pamela Anderson?
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
Shared content-vocabularies:Ontologies
Formal,
explicit specification
of a shared
conceptualisation Abstract model ofsome domain
Consensualknowledge
concepts, properties,relations, functions
machineprocessable
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),
What’s inside an ontology?
terms + specialisation hierarchy classes + class-hierarchy instances slots/values inheritance (multiple? defaults?) restrictions on slots (type, cardinality) properties of slots (symm., trans., …) relations between classes (disjoint, covers) reasoning tasks: classification,
subsumption
Increasing semantic “weight”
NB: we’re not doing philosophy
Ontologies are not
definitive descriptions of what exists in the world (= philosphy)
Ontologies are
models of the worldconstructed
to facilitate communication
Yes, ontologies exist(because we build them)
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
Stack of languagesXML:
Surface syntax, no semanticsXML Schema:
Describes structure of XML documentsRDF:
Datamodel for “relations” between “things”RDF Schema:
RDF Vocabular Definition LanguageOWL:
A more expressive Vocabular Definition Language
RDF Triples in Life Sciences
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
Bluffer’s guide to RDF (2) Every identifier is a URL
= world-wide unique naming!
Has XML syntax
Any statement can be an object• graphs can be nested
pers05 ISBN...Author-of
NYT claims
<rdf:Description rdf:about=“#pers05”> <authorOf>ISBN...</authorOf></rdf:Description>
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
Stack of languagesXML:
Surface syntax, no semanticsXML Schema:
Describes structure of XML documentsRDF:
Datamodel for “relations” between “things”RDF Schema:
RDF Vocabular Definition LanguageOWL:
A more expressive Vocabular Definition Language
OWL: things RDF Schema can’t doequalityenumerationnumber restrictions
Single-valued/multi-valued Optional/required values
inverse, symmetric, transitiveboolean algebra
Union, complement…
OWL: more expressivity
Full
DL
Lite
OWL Full Allow meta-classes etc
OWL DLNegationDisjunctionFull CardinalityEnumerated types
OWL Light(sub)classes, individuals(sub)properties, domain, rangeconjunction(in)equalitycardinality 0/1datatypesinverse, transitive, symmetrichasValuesomeValuesFromallValuesFrom
RDF Schema
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”
exploit existing legacy-data Amazon Lab equipment?
side-effect from user interaction MIT Lab photo-annotator
NOT from manual effortWeb 2.0 community/social interaction
Question:Who writes the meta-data ?
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
Some working examples?
• DOPE• HCLS (http://www.w3.org/2001/sw/hcls/)
DOPE: BackgroundVertical Information Provision
Buy a topic instead of a Journal ! Web provides new opportunities
Business driver: drug development Rich, information-hungry market Good thesaurus (EMTREE)
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
RDF Schema
EMTREE
Queryinterface
RDF
Datasource 1
RDF
Datasource n….
Architecture:
Architecture:
GUI: Spectacle (Aduna)
Metadata Server(Collexis)
EMTREEThesaurus
(RDFS)
Mediator: Sesame (Aduna)
http requests
Java Client
SOAP
DocumentModel(RDFS) Source
Model(RDF)
SeRQL
Additional Source of Data
SourceModel(RDF)SeRQL
GeneThesaurus
(RDFS)
Summarising… Data integration on the Web:
machine processable data besides human processable data
Syntax for meta-data XML (not much meaning) RDF (some meaning) RDF Schema (some meaning) OWL (more meaning
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)