Amit Sheth, "Semantic Interoperability and Information Brokering in Global Information Systems," Keynote talk at IEEE-Metadata Conference, Bethesda, MD, USA, April 6, 1999.

1. Bethesda, Maryland, April 6, 1999 Amit Sheth Large Scale Distributed Information Systems Lab University of Georgia http://lsdis.cs.uga.edu Semantic Interoperability and Information Brokering in Global Information Systems

2. Three perspectives to GlobIS Information Integration Perspective distribution autonomy heterogeneity Information Brokering Perspective data meta-data semantic (terminological, contextual) “ Vision” Perspective data connectivity computing information knowledge

3. Evolving targets and approaches in integrating data and information (a personal perspective) Infocosm Mermaid DDTS Multibase, MRDSM, ADDS, IISS, Omnibase, ... Generation I 1980s DL-II projects ADEPT, InfoQuilt Generation III 1997... InfoSleuth, KMed, DL-I projects Infoscopes, HERMES, SIMS, Garlic,TSIMMIS,Harvest, RUFUS,... Generation II 1990s VisualHarness InfoHarness a society for ubiquitous exchange of (tradeable) information in all digital forms of representation; information anywhere, anytime, any forms

7. (characterization of schematic conflicts in multidatabase systems) Sheth & Kashyap, Kim & Seo Generation I Schematic Conflicts Generalization Conflicts Aggregation Conflicts Abstraction Level Incompatibility Data Value Attribute Conflict Entity Attribute Conflict Data Value Entity Conflict Schematic Discrepancies Naming Conflicts Database Identifier Conflicts Schema Isomorphism Conflicts Missing Data Items Conflicts Entity Definition Incompatibility Naming Conflicts Data Representation Conflicts Data Scaling Conflicts Data Precision Conflicts Default Value Conflicts Attribute Integrity Constraint Conflicts Domain Definition Incompatibility Known Inconsistency Temporal Inconsistency Acceptable Inconsistency Data Value Incompatibility B U T these techniques for dealing with schematic heterogeneity do not directly map to dealing with much larger variety of heterogeneous media

10. (limited types of metadata, extractors, mappers, wrappers) Generation II METADATA EXTRACTORS Find Marketing Manager positions in a company that is within 15 miles of San Francisco and whose stock price has been growing at a rate of at least 25% per year over the last three years Junglee, SIGMOD Record, Dec. 1997 Global/Enterprise Web Repositories Digital Maps Nexis UPI AP Documents Digital Audios Data Stores Digital Videos Digital Images . . . . . . . . .

12. VisualHarness – an example

14. GIS Data Representation – Example multiple heterogeneous metadata models with different tag names for the same data in the same GIS domain Kansas State FGDC Metadata Model Theme keywords : digital line graph, hydrography, transportation... Title : Dakota Aquifer Online linkage : http://gisdasc.kgs.ukans.edu/dasc/ Direct Spatial Reference Method: Vector Horizontal Coordinate System Definition: Universal Transverse Mercator … … … ... UDK Metadata Model Search terms : digital line graph, hydrography, transportation... Topic : Dakota Aquifer Adress Id: http://gisdasc.kgs.ukans.edu/dasc/ Measuring Techniques: Vector Co-ordinate System: Universal Transverse Mercator … … … ...

16. Information Brokering: An Enabler for the Infocosm INFORMATION/DATA OVERLOAD INFORMATION PROVIDERS Newswires Universities Corporations Research Labs Information System Data Repository Information System INFORMATION CONSUMERS Corporations Universities People Government Programs User Query User Query User Query arbitration between information consumers and providers for resolving information impedance INFORMATION BROKERING Information System Data Repository Information System Information Request Information Request Information Request dynamic reinterpretation of information requests for determination of relevant information services and products — dynamic creation and composition of information products

17. Information Brokering: Three Dimensions Objective: Reduce the problem of knowing structure and semantics of data in the huge number of information sources on a global scale to: understanding and navigating a significantly smaller number of domain ontologies S E M A N T I C S S T R U C T U R E S Y N T A X S Y S T E M C O N S U M E R S B R O K E R S P R O V I D E R S D A T A M E T A D A T A V O C A B U L A R Y T H R E E D I M E N S I O N S

18. What else can Information Brokering do? W W W a confusing heterogeneity of media, formats (Tower of Babel) information correlation using physical (HREF) links at the extensional data level location dependent browsing of information using physical (HREF) links user has to keep track of information content !! W W W + Information Brokering Domain Specific Ontologies as “semantic conceptual views” Information correlation using concept mappings at the intensional concept level Browsing of information using terminological relationships across ontologies Higher level of abstraction, closer to user view of information !!

19. Concepts, tools and techniques to support semantics context media-independent information correlations semantic proximity inter-ontological relations ontologies (esp. domain-specific) profiles domain-specific metadata

22. Heterogeneity... … is a Babel Tower!! SEMANTIC INTEROPERABILITY metadata ontologies contexts SEMANTIC HETEROGENEITY

24. InfoQuilt Project: using the M etadata REF erence link http://lsdis.cs.uga.edu/proj/iq/iq.html MREF Complements HREF, creating a “logical web” through media independent ontology & metadata based correlation It is a description of the information asset we want to retrieve MREF domain ontologies IQ_Asset ontology + extension ontologies attributes relations constraints keywords content attributes (color, scene cuts, …) Semantic Correlation using MREF MREF Concept Model for logical correlation using ontological terms and metadata Framework for representing MREF’s Serialization (one implementation choice) X M L M R E F R D F

25. Domain Specific Correlation – example Potential locations for a future shopping mall identified by all regions having a population greater than 5000, and area greater than 50 sq. ft. having an urban land cover and moderate relief <A MREF ATTRIBUTES(population > 5000; area > 50; region-type = ‘block’; land-cover = ‘urban’; relief = ‘moderate’) can be viewed here </A> => media-independent relationships between domain specific metadata : population, area, land cover, relief => correlation between image and structured data at a higher domain specific level as opposed to physical “link- chasing” in the WWW domain specific metadata: terms chosen from domain specific ontologies Population: Area : Land cover: Relief: Boundaries: Census DB TIGER/Line DB US Geological Survey Regions (SQL) :  Boundaries  Image Features (image processing routines)

26. Domain Specific Correlation – example

27. A DL II approach for Information Brokering CONSTRUCTING ADDITIONAL META-INFORMATION RESOURCES Physical/Simulation World DISCOVERING COLLECTIONS OF HETEROGENEOUS INFORMATION AND META-INFORMATION RESOURCES Images Data Stores Documents Digital Media Domain Specific Ontologies Domain Independent Ontologies Iscape N CONSTRUCTING APPROPRIATE INFORMATION LANDSCAPES Iscape 1

29. Putting MREFs to work User Agent Profile Manager user information MREF request retrieve profile User display results change profile design MREF domain ontologies MREF Builder IQ_Asset ontology + extension ontologies construct new MREF Broker Agent send MREF send results retrieve MREF retrieve MREF MREF repository MREF repository User profiles

32. Using c-contexts to reason about information in database - Reasoning with c-contexts: glb(C def (DOC), C Q ) - Ontological Inferences: - DocumentConcept - (hasOrganization, { “USGS” } ) Challenge 1: use of multiple ontologies Challenge 2: estimating the loss of information EXAMPLE C def (DOC) <(hasOrganization, AgencyConcept)> C Q <(hasOrganization, { “USGS”} )> glb(C def (DOC), C Q ) <(self, DocumentConcept),(hasOrganization, { “USGS” } )>

33. Estimating information loss for multi-ontology based query processing in the OBSERVER/InfoQuilt system OBSERVER architecture Eduardo Mena (III’98) Data Repositories Mappings Ontologies COMPONENT NODE Data Repositories Mappings Ontologies COMPONENT NODE Data Repositories Mappings Ontology Server Query Processor User Query Ontologies USER NODE Interontologies Terminological Relationships IRM IRM NODE Ontology Server Ontology Server Query Processor Query Processor

34. Estimating information loss for multi-ontology based query processing in the OBSERVER/InfoQuilt system “ Get title and number of pages of books written by Carl Sagan” Query construction - Example Eduardo Mena (III’98) User ontology: WN [name pages] for (AND book (FILLS creator “Carl Sagan”)) Target ontology: Stanford-I Integrated ontology WN-Stanford-I [title number-of-pages] for (AND book (FILLS doc-author-name “Carl Sagan”)) Ontologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.html http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/

35. Re-use of Knowledge: Bibliography Data Ontology Estimating information loss for multi-ontology based query processing in the OBSERVER/InfoQuilt system “ Get title and number of pages of books written by Carl Sagan” Query construction - Example Eduardo Mena (III’98) Biblio-Thing Document Book Edited-Book Technical-Report Periodical-Publication Journal Magazine Newspaper Miscellaneous-Publication Technical-Manual Computer-Program Multimedia-Document Artwork Cartographic-Map Thesis Doctoral-Thesis Master-Thesis Proceedings Conference Agent Person Author Organization Publisher University Stanford-I User ontology: WN [name pages] for (AND book (FILLS creator “Carl Sagan”)) Target ontology: Stanford-I Integrated ontology WN-Stanford-I [title number-of-pages] for (AND book (FILLS doc-author-name “Carl Sagan”)) Ontologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.html http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/

36. Re-use of Knowledge: A subset of WordNet 1.5 Estimating information loss for multi-ontology based query processing in the OBSERVER/InfoQuilt system “ Get title and number of pages of books written by Carl Sagan” Query construction - Example Eduardo Mena (III’98) User ontology: WN [name pages] for (AND book (FILLS creator “Carl Sagan”)) Target ontology: Stanford-I Integrated ontology WN-Stanford-I [title number-of-pages] for (AND book (FILLS doc-author-name “Carl Sagan”)) Ontologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.html http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/ Print-Media Press Publication Journalism Newspaper Magazine Book Periodical Trade-Book Brochure TextBook Reference-Book SongBook PrayerBook Pictorial Series Journals CookBook Instruction-Book WordBook HandBook Directory Annual Encyclopedia Manual Bible GuideBook Instructions Reference-Manual

37. WN ontology and user query Estimating information loss for multi-ontology based query processing in the OBSERVER/InfoQuilt system “ Get title and number of pages of books written by Carl Sagan” Query construction - Example Eduardo Mena (III’98) User ontology: WN [name pages] for (AND book (FILLS creator “Carl Sagan”)) Target ontology: Stanford-I Integrated ontology WN-Stanford-I [title number-of-pages] for (AND book (FILLS doc-author-name “Carl Sagan”)) Ontologies sites: http://www.cogsci.princeton.edu/~wn/w3wn.html http://www-ksl.stanford.edu/knowledge-sharing/ontologies/html/bibliographic-data/

39. Estimating information loss for multi-ontology based query processing in the OBSERVER/InfoQuilt system Loss of information based on intensional information Eduardo Mena (III’98) User Query: (AND book (FILLS doc-author-name “Carl Sagan”)) Plan 1: (AND document (FILLS doc-author-name “Carl Sagan”)) book:=(AND publication ( AT-LEAST 1 ISBN )) publication:=(AND document ( AT-LEAST 1 place-of-publication )) Loss: “Instead of books written by Carl Sagan, OBSERVER is providing all the documents written by Carl Sagan (even if they do not have an ISBN and place of publication)”

40. Estimating information loss for multi-ontology based query processing in the OBSERVER/InfoQuilt system Example: loss for the plans Eduardo Mena (III’98) Plan 1: (AND document (FILLS doc-author-name “Carl Sagan”)) [case 2] 91.57% < (1-Loss) < 91.75% Plan 2: (AND periodical-publication (FILLS doc-author-name “Carl Sagan”)) 94.03% < (1-Loss) < 100% [case 3] Plan 3: (AND journal (FILLS doc-author-name “Carl Sagan”)) [case 3] 98.56% < (1-Loss) < 100% Plan 4: (AND UNION(book, proceedings, thesis, misc-publication, technical-report) (FILLS doc-author-name “Carl Sagan”)) [case 1] 0% < (1-Loss) < 7.22%

41. Summary Text Structured Databases Data Syntax, System Federated DB Semi-structured Metadata Structural, Schematic Mediator, Federated IS Visual, Scientific/Eng. Knowledge Semantic Knowledge Mgmt., Information Brokering, Cooperative IS