1. Ontologies: Introduction and Some Uses
Boyan Brodaric
Bertram Ludäscher

2. Uses of Concept Spaces / Ontologies
Concept Browsing and Searching
find concept C,
find all related concepts D; display: C ==R==>D
“Smart Data Discovery”
find instances of data sets X that are related to C:
X = {....your-tagged-data-here...} ==R==> C
searching for instances of D ...
... and knowing that C ==IS-A==> D
... we can find X !
=> requires “Smart Source Registration”
Integrated Views and Querying
access, iterate over, aggregate, group-by, ... concepts

3. Glue Knowledge for Semantic Mediation: Unified Medical Language System (UMLS)
Started by National Library of Medicine in 1986
... to aid the development of systems that help health professionals and researchers retrieve and integrate electronic biomedical information from a variety of sources and to make it easy for users to link disparate information systems, including computer-based patient records, bibliographic databases, factual databases, and expert systems.
The UMLS project develops "Knowledge Sources" that can be used by a wide variety of applications programs to overcome retrieval problems caused by differences in terminology and the scattering of relevant information across many databases.

4. Medical Subject Headings (MeSH) Tree Structures

5. Finding out about .... Paleontology
Find *paleo*
Find related concepts

6. Combining Ontologies: UMLS and Gene Ontology

7. UMLS Concept Space as Relational Tables
concept(CUI, LUI, SUI, STR)
CUI = concept ID
LUI = lexical ID
SUI = string ID
STR = string representation
relationship(CUI1, REL, CUI2, RELA, SAB, SL)
REL = {chd (child), par (parent), sib (sibling), ...}
RELA = {isa, has_part, adjacent_to, contains, contained_in... }
SAB,SL = origin of definition (MeSH2001)

8. Model-Based Mediator Architecture
(XML-Wrapper)
CM-Wrapper
USER/Client
CM (Integrated View)
Mediator
Engine
FL rule proc.
LP rule proc.
Graph proc.
XSB Engine
CM(S) =
OM(S)+KB(S)+CON(S)
GCM
CM S1
CM S2
CM S3
CM Queries & Results
(exchanged in XML)
Domain Maps
DMs
Process Maps
PMs
“Glue” Maps
GMs
semantic
context
CON(S)
Integrated View
Definition IVD
First results & Demos:
KIND prototype, formal
DM semantics, PMs
[SSDBM00] [VLDB00]
[ICDE01] [NIH-HB01]
[BNCOD02] [ER02]
[EDBT02] [BioInf02]

9. Source Contextualization & DM Refinement
In addition to registering
(“hanging off”) data relative to
existing concepts, a source
may also refine the mediator’s
domain map...
sources can register new concepts at the mediator ...

10. Demonstration: Using Ontologies in Queries/Views
find data sets that are “inside” X
inside =
logical_inside PLUS spatially_insde
logical_inside uses
UMLS, and
NEURONAMES
spatially_inside uses
Oracle-Spatial
client

11. Mediator View Definition
Query Processing Demo
Contextualization
CON(Result) wrt. ANATOM.
Mediator View Definition
DERIVE
protein_distribution(Protein, Organism,Brain_region, Feature_name, Anatom, Value)
WHERE
I:protein_label_image[ proteins ->> {Protein}; organism -> Organism; anatomical_structures ->>
{AS:anatomical_structure[name->Anatom]}] , % from PROLAB
NAE:neuro_anatomic_entity[name->Anatom; % from ANATOM
located_in->>{Brain_region}],
AS..segments..features[name->Feature_name; value->Value].
provided by the domain expert and mediation engineer
deductive OO language (here: F-logic)
Query results
in context

12. Inside Query Evaluation: Another Example
"How does the parallel fiber output (Yale/SENSELAB) relate to the distribution of Ryanodine Receptors (UCSD/NCMIR)?”
push selection
@SENSELAB: X1 := select targets of “output from parallel fiber” ;
determine source context
@MEDIATOR: X2 := “find and situate” X1 in ANATOM Domain Map;
compute region of interest (here: downward closure)
@MEDIATOR: X3 := subregion-closure(X2);
@NCMIR: X4 := select PROT-data(X3, Ryanodine Receptors);
compute protein distribution
@MEDIATOR: X5 := compute aggregate(X4);
display in context
@MEDIATOR/GUI: display X5 in context (ANATOM)

13. Ecological Metadata Language (EML): Useful for Marking up GEON Data?