1. All Hands Meeting 2003 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 ) 1.Controlled Vocabularies  agreed upon range of values (“enumeration type”)  e.g. “standard names” for materials, diseases, … 2.Simple Taxonomies, Classification hierarchies (isa)  controlled concept vocabulary + subconcept (specialization) relationship  e.g. biological taxonomies 3.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, … 4.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 relationship concept isa concept Parent  Person  offspring.Person class relation part-of

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 concept relationship 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) 1.Upload ontologies O1, O2, … (O2, O3 use O1,…) 2.Upload ontology mapping Om :: Oa  Ob 3.Register data sets D1, D2, … to ontology Oa 4.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 Navigating through Multi-resolution information Linking animal and human imaging data brain cerebellum cerebellar cortex Purkinje cell dendritic spine Entopeduncular nucleus Globus pallidus, internal segment Animal Model Disease Process Link database concepts to UMLS/Neuronames Utilize the neurohomology ontology: M. Bota at USC Develop disease and animal model knowledge maps

24. Knowledge Maps Parkinson’s Disease Pathological feature Alpha synuclein Abnormal filaments Substantia nigra ubiquitin symptom tremor akinesia rigidity Motor deficit neurons Lewy Body C0085200 C0030567 Cell inclusion C0205708 glia neuronal degeneration Dopamine neuron cortex Basal forebrain Filamentous inclusion C0230674 C0815003 C024566 C0027746 C0746626 C027746 C0007776 C0175412 C0041538 C0027882 C0027836

25. Knowledge Map: Animal Model a-synuclein mouse transgenic animal Cellular phenotype Cellular inclusion Behavioral phenotype Alpha synuclein C024566 nuclear inclusion C0544907 Alpha synuclein C024566 Cytoplasmic inclusion C0205708 Motor deficit C0746626 ubiquitin C0041538 neurons glia C0027882 C0027836 C0025936

26. Knowledge Maps Parkinson’s Disease Pathological feature Alpha synuclein neurons Lewy Body Cytoplasmic inclusion glia Filamentous inclusion a-synuclein mouse Cellular phenotype Cellular inclusion nuclear inclusion ubiquitin Alpha synuclein neurons Cytoplasmic inclusion ubiquitin

27. Parkinson’s disease map diseaseC0012634 course of illnessC0242656 disease phaseC0457338 pathologyC0677042 disease characteristicC0599878 symptomsC0683368 Pathological processC0030660 sign/symptomC0037088 proneness/riskC0178598 severitiesC0439793 epidemiologyC0699910 disease classificationC0683326 prevention, intervention and treatmentC0679677

28. Parkinson’s disease features Concept A relationshipConcept B

29. Parkinson’s disease processes Concept ArelationshipConcept 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@nbirn.net with subject/bodymajordomo@nbirn.net “subscribe birn-ontologies”