1. Model-Based Mediation with Domain Maps Bertram Ludäscher * Amarnath Gupta * Maryann E. Martone + * San Diego Supercomputer Center (SDSC) + National Center for Microscopy and Imaging Research (NCMIR) University of California, San Diego (UCSD)

2. Overview Motivation –Problem with current Mediator Architecture –Complex Scientific Multiple-World Scenarios Model-Based Mediation Architecture –Lifting from XML to level of Conceptual Models (CMs) Formal Framework –Domain Maps (DMs) –Generic Conceptual Model GCM –Integrated View Definition Example Query Evaluation Open Issues

3. A Standard Mediator Architecture (MIX -- Mediation of Information using XML, SDSC/UCSD) MIX MEDIATOR INTEGRATED VIEWUSER-Query Data Sources DB Files WWW Lab1Lab2Lab3 Wrapper XML Q/A XML Integrated View Definition XMAS/XQuery XML Q/A

4. The Problem: Complex Multiple-World Scenarios Current Integration Issues –Structural/Schema Conflicts common semistructured data model (XML) schema transformations/integration (XML queries & transforms) – Limited Query Capabilities capability based rewriting (e.g., TSIMMIS) –... BUT scenarios are “one-world” ( amazon.com vs. bn.com ) or simple multiple world ( home buyer ) Problem: No Support for Semantic Mediation –“complex multiple-world” scenarios (Neuroscience, Geoscience): complex, disjoint, seemingly unrelated data “hidden semantics” in complex, indirect relationships

5. A Neuroscience Question What is the cerebellar distribution of rat proteins with more than 70% homology with human NCS-1? Any structure specificity? How about other rodents? protein localization (NCMIR) Wrapper neurotransmission (SENSELAB) Wrapper morphometry (SYNAPSE) Wrapper ??? Integrated View ??? ???Mediator ??? ??? Integrated View Definition ???

6. Hidden Semantics: Protein Localization (NCMIR) RyR …. spine 0 branchlet 30 Molecular layer of Cerebellar Cortex Purkinje Cell layer of Cerebellar Cortex Fragment of dendrite

7. Hidden Semantics: Morphometry (SNYAPSE) … 12.348 1.93 4.47 9.884 7.930 4.47 1.79 … Branch level beyond 4 is a branchlet Must be dendritic because Purkinje cells don’t have somatic spines

8. Approach: Model-Based Mediation Complex Multiple Worlds Integration Problem –terms not directly joinable –complex, indirect associations –unstated, “hidden” semantics (not just schema conflicts) Missing “Semantic Link” => how to define complex, indirect semantic links? => lift mediation to the level of conceptual models (CMs) => domain expert’s knowledge formalized as rules over CMs => Model-Based Mediation

9. XML-Based vs. Model-Based Mediation IF  THEN  Logical Domain Constraints Integrated-CM := CM-QL(Src1-CM,...)...... (XML) Objects Conceptual Models C2 C3 C1 R Classes, Relations, is-a, has-a,... DOMAIN MAP Raw Data XML Elements XML Models Integrated-DTD := XQuery(Src1-DTD,...) No Domain Constraints A = (B*|C),D B =... Structural Constraints (DTDs), Parent, Child, Sibling,...

10. Extended Mediator Architecture Wrappers export Conceptual Models (CMs) –facts & rules for classes, relationships, ICs,... –source data is “put into context” (“aboutness” index) by linking to domain maps (DMs) Mediator employs CMs and DMs –... to define complex semantic relationships on the formalized domain knowledge Generic Conceptual Model (GCM) –as a common target CM –minimal requirements/core expressions: instance(O,C), subclass(C1,C2) method_type(C,M,C’), method_value(O,M,R) relation_type(R,A1/C1,...,An/Cn) relation_value(R,a1,...,an) Expressiveness, Extensibility –allow inductive properties (inheritance, closures,...) –employ a declarative rule language (e.g. F-Logic)

11. Model-Based Mediator Architecture USER/Client USER/Client S1 S2 S3 XML-Wrapper CM-Wrapper XML-Wrapper CM-Wrapper XML-Wrapper CM-Wrapper GCM CM S1 GCM CM S2 GCM CM S3 CM (Integrated View) Mediator Engine FL rule proc. LP rule proc. Graph proc. XSB Engine Domain Map DM Integrated View Definition IVD Logic API (capabilities) CM Queries & Results (exchanged in XML) CM Plug-In

12. Formalizing Domain Knowledge: Domain Map for SYNAPSE and NCMIR A domain map comprises Description Logic facts... - concepts ("classes") - roles ("associations") derived properties...... expressed as logic rules - (e.g. F-logic) domain map Purkinje cells and Pyramidal cells have dendrites that have higher-order branches that contain spines. Dendritic spines are ion (calcium) regulating components. Spines have ion binding proteins. Neurotransmission involves ionic activity (release). Ion-binding proteins control ion activity (propagation) in a cell. Ion-regulating components of cells affect ionic activity (release). domain expert knowledge equivalent Description Logic facts

13. Domain Map Refinement In addition to registering (“hanging off”) data, a source may also refine the mediator’s domain map...... source can register new concepts at the mediator...

14. Definition of Integrated Views (Deja Vu?)... XML/CM-2-FL Translators <!ELEMENT Study (study_id, … animal, experiments, experimenters> <!ELEMENT experiment (description, instrument, parameters)> studyDB[studies =>> study]. study[study_id => string; … animal => animal; experiments =>> experiment; experimenters =>> string]. … Specification of Domain Knowledge Subclasses Data Classification Integrity Constraints mushroom_spine :: spine DERIVE S:mushroom_spine FROM S:spine[head  _; neck  _]. ic1(S):ALERT[type  “invalid spine”; object  S] IF S:spine[undef ->> {head, neck}].

15. ... Definition of Integrated Views (Multiple Sources) Integrated View Definition Schema Reasoning & Dynamic Classes taxon[subspecies  string; species  string; genus  string; … phylum  string; kingdom  string; superkingdom  string]. subspecies::species::genus:: … kingdom::superkingdom TAXON Rank Hierarchy DERIVE T:TR, TR::TR1 FROM T: ‘TAXON’.taxon[Taxon_Rank  TR, Taxon_Rank1  TR1], Taxon_Rank::Taxon_Rank1. Create Classes from TAXON data DERIVE protein_distribution(Protein, Organism,Brain_region,Feature_name,Anatom,Value) FROM I:protein_label_image[ proteins ->> {Protein}; organism -> Organism; anatomical_structures ->> {AS:anatomical_structure[name->Anatom]}], % from PROLAB AS..segments..features[name->Feature_name; value->Value], NAE:neuro_anatomic_entity[name-> Anatom; % from ANATOM located_in->>{Brain_region}]. TAXON DB Schema

16. Query Evaluation Example push selection @SENSELAB: X1 := select output from parallel fiber ; determine source context @MEDIATOR: X2 := “hang off” X1 from 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); "How does the parallel fiber output (Yale/SENSELAB) relate to the distribution of Ryanodine Receptors (UCSD/NCMIR)?"

17. ANATOM Domain Map with Registered Data ANATOM DATA

18. Deductive Closure of “has_a” with “tc(is_a)”: (YES -- Real Recursive Views!! ;-) ANATOM CLOSURE

19. Interactive Queries KIND01

20. Resulting Sub DOMAIN MAP “Browser” PROTLOC

21. Computed Protein Localization Data PROTLOC

22. Client-Side Result Visualization (using AxioMap Viewer: Ilya Zaslavsky) PROTLOC-AxioMap

23. Comparison & Summary: Model-Based Mediation

24. Conclusions and Outlook Model-based Mediation Architecture –for complex multiple worlds scenarios (Neuroscience,...) –sources export CMs (data “lifted” to conceptual level) –mediator employs DMs (“semantic road map”) Simple Prototype based on XSB/FLORA –source and result data situated in DM context –domain scientists are excited... Some Open Issues –striking the right balance between complexity and expressiveness of DMs (e.g. subsumption and satisfiability of DMs should be decidable) –query processing/optimization –modeling query capabilities –semantic annotation tools for “dumb” sources –re-implement... *sigh*... –...

25. ADDITIONAL MATERIAL STARTS HERE

26. ANATOM Domain Map ANATOM

27. Model-Based Mediation with DOMAIN MAPS (DMs) Integrated-CM(Z1,...) := get X1,... from Src1; get X2,... from Src2; LINK (Xi, Yj); Zj = CM-QL(X1,...,Y1,...) LINK(X,Y): X.zip = Y.zip X.addr in Y.zip X.zip overlaps Y.county... “Semantic Road Maps” for situating source data => navigational aid (browsing source classes at the conceptual level) => basis for integrated views across multiple worlds => link points (concepts) and labeled arcs (roles) => formal semantics (in FL and/or DLs) Example: ANATOM DM = antatomical entities (concepts) + is_a, has_a, overlaps,... (roles) => from syntactic equality to semantic joins

28. Example Query Evaluation (I) Example: protein_distribution –given: organism, protein, brain_region –ANATOM DM: recursively traverse the has_a_star paths under brain_region collect all anatomical_entities –Source PROLAB: join with anatomical structures and collect the value of attribute “image.segments.features.feature.protein_amount” where “image.segments.features.feature.protein_name” = protein and “study_db.study.animal.name” = organism –Mediator: aggregate over all parents up to brain_region report distribution

29. Interactive Queries KIND

30. Summary & Outlook: Federation of Brain Data CCBCCB, Montana SU Surface atlas, Van Essen LabVan Essen Lab NCMIRNCMIR, UCSD stereotaxic atlas LONILONI MCell, CNL, SalkCNL ANATOM PROTLOC ResultResult (VML) ResultResult (XML/XSLT)  MODEL-BASED Mediation