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BIRN ONTOLOGIES Session

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BIRN ONTOLOGIES Session Jeffrey Grethe Amarnath Gupta Bertram Lud scher Maryann E. Martone Overview First half: Ontologies (brief; not a total recall ) (Bertram ... – PowerPoint PPT presentation

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Title: BIRN ONTOLOGIES Session


1
BIRN ONTOLOGIES Session
  • Jeffrey Grethe
  • Amarnath Gupta
  • Bertram Ludäscher
  • Maryann E. Martone

2
Overview
  • First half
  • Ontologies (brief not a total recall)
    (Bertram)
  • UMLS Bonfire extensions (Jeff)
  • Disease maps (Maryann, Amarnath)
  • Ontology-enhanced tools (Maryann)
  • Second half
  • Discussion on policy issues, BIRN ontology
    curation etc

3
Kinds of Ontologies (simplified cheat sheet)
  • Controlled Vocabularies
  • agreed upon range of values (enumeration type)
  • e.g. standard names for materials, diseases,
  • Simple Taxonomies, Classification hierarchies
    (isa)
  • controlled concept vocabulary
  • subconcept (specialization) relationship
  • e.g. biological taxonomies
  • Graph-like Ontologies
  • isa, has-a, and other relationships
  • (contained_in, causes, activates, ),
  • the latter usually w/o formalized semantics (but
    agreed upon)
  • e.g. semantic nets, RDF,
  • Full-fledged Ontologies
  • Usually logic-based ontologies relationships
    (including isa) are logic consequences of formal
    concept definitions ? (partially) defined
    semantics
  • You knew this already e.g. 5. Conceptual Models
  • DSM-IV(Diagnostic and Statistical Manual of
    Mental Disorders )
  • 290.11 isa 290.1 isa 290

concept
part-of
isa
concept
concept
Parent ? Person??offspring.Person
4
DSM IV Taxonomy
290 Dementia of the Alzheimer's Type, With Late
Onset, Uncomplicated 290.1 Dementia Due to
Creutzfeldt-Jakob Disease 290.1 Dementia Due
to Pick's Disease 290.1 Dementia of the
Alzheimer's Type, With Early Onset, Uncomplicated
290.11 Dementia of the Alzheimer's Type, With
Early Onset, With Delirium 290.12 Dementia of
the Alzheimer's Type, With Early Onset, With
Delusions 290.13 Dementia of the Alzheimer's
Type, With Early Onset, With Depressed Mood
290.2 Dementia of the Alzheimer's Type, With
Late Onset, With Delusions 290.21 Dementia of
the Alzheimer's Type, With Late Onset, With
Depressed Mood 290.3 Dementia of the
Alzheimer's Type, With Late Onset, With Delirium
290.4 Vascular Dementia, Uncomplicated 290.41
Vascular Dementia, With Delirium 290.42
Vascular Dementia, With Delusions 290.43
Vascular Dementia, With Depressed Mood 291
Alcohol Intoxication Delirium
5
Uses of Ontologies in Data Integration
  • Smart (conceptual-level) data discovery,
    browsing, querying
  • looking for C, finding D (which is C-related)
  • terminological and semantic glue between
    different data worlds
  • Conceptual / Semantic Modeling of a domain
  • for terminologies domain maps (in description
    logic)
  • for processes process maps, disease maps
    (open issue)
  • Need to provide
  • Ontology exchange syntax
  • Ontology extensions mechanisms (? BONFIRE)
  • Inter-ontology mapping mechanisms (?yours vs.
    mine)
  • Data-to-ontology registration mechanisms (?data
    to concepts)

6
Generic Standards
  • RDF, RDFS
  • For graph-based ontologies (explicit statements)
  • OWL (Web Ontology Language)
  • Three levels OWL Lite, OWL DL, OWL Full
  • Some Features
  • Ontology O2 uses (refers to) O1 (over the web!)
  • ? formalism to exchange, extended, and map
    between ontologies

Parent ? Person??offspring.Person
7
Generic Tools
  • Ontology authoring Protégé-2000 ontology tool
    (Stanford)
  • OWL Plug-In (evolving)
  • Developers corner
  • Jena-2 Semantic Web Framework (HP), for dealing
    with OWL ontologies
  • Logic programming extensions (SWI)

8
Example Ontology-enhanced Map Integration (OMI)
  • Upload ontologies
  • O1, O2, (O2, O3 use O1,)
  • Upload ontology mapping
  • Om Oa ? Ob
  • Register data sets
  • D1, D2, to ontology Oa
  • Query data sets through Ob interface!

9
  • UMLS
  • BONFIRE (Community Ontology Building)

10
What is UMLS?
  • UMLS is a long-term research project began on
    1986 by the National Library of Medicine (NLM)
  • UMLS is a collection of knowledge sources
    designed to facilitate the retrieval and
    integration of information from multiple
    machine-readable biomedical information sources

11
Knowledge Resources
  • Metathesaurus
  • The Metathesaurus is organized by concept or
    meaning, it provides a uniform, integrated
    distribution format from about 60 biomedical
    vocabularies and classifications and links many
    different names for the same concepts
  • The 2000 edition of the Metathesaurus includes
    more than 730,000 concepts and 1.5 million
    concept names from over 50 different biomedical
    vocabularies, some in multiple languages

12
Knowledge Resources
  • Semantic Network
  • Semantic Network contains information about the
    types or categories (e.g., "Disease or Syndrome,"
    "Virus") to which all concepts have been assigned
    and the permissible relationships among these
    types (e.g., "Virus" causes "Disease or
    Syndrome")
  • The semantic types are the nodes in the Network,
    and the relationships between them are the
    links.It has 132 semantic types, 53 links between
    the semantic types.

13
Knowledge Resources
  • Semantic Network
  • Semantic types organisms, anatomical structures,
    biologic function, chemicals, events, physical
    objects, and concepts or ideas etc.
  • Relations isa, physically related to,'
    spatially related to,' temporally related to,'
    functionally related to,' and conceptually
    related to etc.

14
Hierarchical relations types
15
Associative (non-isa) Relationships
16
Knowledge Resources
  • Information Sources Map
  • The information sources are varied and include
    bibliographic databases, diagnostic expert
    systems, and factual databases
  • The Information Sources Map or directory contains
    both human-readable and machine-"processable"
    information about the scope, location,
    vocabulary, syntax rules, and access conditions
    of biomedical databases of all kinds

17
Related Sites
  • Further Information
  • http//www.nlm.nih.gov/
  • http//www.nlm.nih.gov/research/umls/
  • http//www.nlm.nih.gov/research/umls/UMLSDOC.HTML
  • http//www.nlm.nih.gov/research/umls/umlsmain.html

18
BONFIRE
  • BONFIRE will allow BIRN users to accommodate
    concepts not present in the available pre-defined
    source ontologies
  • Whenever possible, users should employ the
    relationship terms provided within the UMLS or
    other source ontologies provided by BIRN
  • Once new terms are defined, when will they will
    become part of the BIRN Ontology (BONFIRE)?
  • after appropriate curation?

19
Ontology Refinement
20
An Example Data Set
  • species rat (UMLS C003493)
  • region neostriatum (UMLS C0162512)
  • cell type medium spiny cell (No Concept
    Available)
  • structure spiny dendrite(No Concept Available)
  • segmented object dendritic spine (UMLS
    C0872341)
  • segmented object dendritic shaft (No Concept
    Available)

21
BONFIRE Example
  • For this data set, no ontology IDs exist for
    medium spiny cell, spiny dendrite or dendritic
    shaft.
  • medium spiny cell (BONFIRE BID006)
  • medium spiny cell is a neuron (UMLS
    C0027882)
  • medium spiny cell has location neostriatum
    (UMLS C0162512)
  • medium spiny cell is a neuron AND has
    property dendritic spine (UMLS C0872341)
  • spiny dendrite (BONFIRE BID007)
  • spiny dendrite is a dendrite (UMLS C0011305)
  • spiny dendrite contains dendritic spine (UMLS
    C0872341)

22
  • DISEASE MAPS

23
Glue Knowledge for Mouse BIRN
Linking animal and human imaging data
Navigating through Multi-resolution information
brain
Entopeduncular nucleus
Globus pallidus, internal segment
cerebellum
Disease Process
Animal Model
cerebellar cortex
Purkinje cell
  • Link database concepts to UMLS/Neuronames
  • Utilize the neurohomology ontology M. Bota at
    USC
  • Develop disease and animal model knowledge maps

dendritic spine
24
Knowledge Maps
Parkinsons Disease
C0030567
Pathological feature
symptom
akinesia
C027746
neuronal degeneration
C0027746
tremor
rigidity
Lewy Body
C0085200
Dopamine neuron
C0815003
Motor deficit
C0746626
Cell inclusion
C0205708
Filamentous inclusion
C0230674
Substantia nigra
C0175412
neurons
C0027882
Abnormal filaments
cortex
C0007776
glia
C0027836
Basal forebrain
ubiquitin
C0041538
Alpha synuclein
C024566
25
Knowledge Map Animal Model
a-synuclein mouse
transgenic animal
C0025936
Cellular phenotype
Behavioral phenotype
Cellular inclusion
Motor deficit
C0746626
nuclear inclusion
Cytoplasmic inclusion
C0544907
C0205708
ubiquitin
C0041538
glia
neurons
C0027836
C0027882
26
Knowledge Maps
Parkinsons Disease
a-synuclein mouse
nuclear inclusion
Cellular phenotype
Pathological feature
Cytoplasmic inclusion
Cellular inclusion
Lewy Body
Alpha synuclein
Filamentous inclusion
neurons
ubiquitin
glia
27
Parkinsons disease map
disease C0012634
course of illness C0242656
disease phase C0457338
pathology C0677042
disease characteristic C0599878
symptoms C0683368
Pathological process C0030660
sign/symptom C0037088
proneness/risk C0178598
severities C0439793
epidemiology C0699910
disease classification C0683326
prevention, intervention and treatment C0679677
28
Parkinsons disease features
Concept A relationship Concept B
29
Parkinsons disease processes
Concept A relationship Concept B
30
Object-Oriented Modeling
31
  • TOOLS
  • Custom Know-ME, OMI,
  • Generic Protégé-2000,

32
  • Discussion

33
Getting Organized
  • Join the mailing list
  • Mail to majordomo_at_nbirn.net with subject/body
  • subscribe birn-ontologies
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