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Ontologies: Introduction and Some Uses

Ontologies: Introduction and Some Uses. Boyan Brodaric Bertram Lud ä scher. Uses of Concept Spaces / Ontologies. Concept Browsing and Searching find concept C, find all related concepts D; display: C ==R ==>D “Smart Data Discovery”

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Ontologies: Introduction and Some Uses

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

  9. In addition to registering (“hanging off”) data relative to existing concepts, a source may also refine the mediator’s domain map... Source Contextualization & DM Refinement • 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 • visualize @ client

  11. 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]. Contextualization CON(Result) wrt. ANATOM. • provided by the domain expert and mediation engineer • deductive OO language (here: F-logic) Query results in context Query Processing Demo

  12. Inside Query Evaluation: Another Example 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); push selection @NCMIR: X4 := select PROT-data(X3, Ryanodine Receptors); compute protein distribution @MEDIATOR: X5 := compute aggregate(X4); display in context @MEDIATOR/GUI: display X5 incontext (ANATOM) "How does the parallel fiber output (Yale/SENSELAB) relate to the distribution of Ryanodine Receptors (UCSD/NCMIR)?”

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

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