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Workshop on Semantic Web:

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Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges by Amit Sheth Director, Large-Scale Distributed Information Systems Lab. – PowerPoint PPT presentation

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Title: Workshop on Semantic Web:


1
by Amit Sheth Director, Large-Scale Distributed
Information Systems Lab. University of Georgia,
Athens, GA USA http//lsdis.cs.uga.edu Founder/
Chairman, Taalee, Inc. http//www.taalee.com Spec
ial thanks, Digital Library project team at
LSDIS
Workshop on Semantic Web Models,
Architecture and Management September 21, 2000
Lisbon, Portugal
2
Semantics The Next step in the Webs Evolution
  • Semantics meaning or relationship of meanings,
    or relating to meaning (Webster), meaning and
    use of data (Information System)
  • Semantic Web The Web of data (and connections)
    with meaning in the sense that a computer program
    can learn enough about what the data means to
    process it. . . .
  • . . . Imagine what computers can understand
    when there is a vast tangle of interconnected
    terms and data that can automatically be
    followed. (Tim Berners-Lee, Weaving the Web,
    1999)

3
Semantic Web
  • A Web in which machine reasoning will be
    ubiquitous and devastatingly powerful.
  • A place where the whim of a human being and
    the reasoning of a machine coexist in an
    ideal, powerful mixture.
  • A semantic Web would permit more accurate
    and efficient Web searches, which are
    among the most important Web-based
    activities.
  • A personal definition
  • Semantic Web The concept that
    Web-accessible content can be organized
    semantically, rather than though syntactic
    and structural methods.

4
Case Studies
  • Markups/Standards DAML Semantic Annotations and
    Directory DSML Directory (of course, XML, RDF,
    namespaces)
  • Commercialization 1 (Oingo) Taxonomy Ontology
    and Semantic Techniques
  • Commercialization 2 (Taalee) Knowledge-base
    (Taxonomy, Domain Modeling, Entities and
    Relationships) and Semantic Techniques
  • Research (Digital Earth at UGA) Complex
    Relationships

5
allow semantic interoperability at the level we
currently have syntactic interoperability in XML
DARPA (and W3C) perspective
  • Create an Agent Mark-Up Language (DAML) built
    upon XML that allows users to provide
    machine-readable semantic annotations for
    specific communities of interest.
  • Create tools that embed DAML markup on to web
    pages and other information sources in a manner
    that is transparent and beneficial to the users.
  • Use these tools to build up, instantiate,
    operate, and test sets of agent-based programs
    that markup and use DAML.
  • 5. 6. .applications

DARPA Agent Mark Up Language (DAML) Program
Manager Professor James Hendler 
http//dtsn.darpa.mil/iso/programtemp.asp?mode347
6
DAML an Example
ltONTOLOGY IDpowerpoint-ontology" VERSION"1.0"
DESCRIPTIONformal model for powerpoint
presentations"gt ltDEF-CATEGORY NAMETitle"
ISAPres-Feature" gt ltDEF-CATEGORY
NAMESubtitle" ISAPres-Feature"
gt ltDEF-RELATION NAMEtitle-of"
SHORT"was written by"gt
ltDEF-ARG POS1 TYPEpresentation"gt
ltDEF-ARG POS2 TYPEpresenter" gt
ltTitlegt DAML ltsubtitlegt an Example
lt/subtitlegt lt/titlegt ltUSE-ONTOLOGY
IDPPT-ontology" VERSION"1.0" PREFIXPP" URL
"http//iwp.darpa.mil/ppt..html"gt ltCATEGORY
NAMEpp.presentation FOR"http//iwp.darpa.mil/j
hendler/agents.html"gt ltRELATION-VALUE POS1
Agents POS2 /madhangt
  • Objects in the web can be marked- in principle -
    (manually or automatically) to include the
    following information
  • Descriptions of data they contain (DBs)
  • Descriptions of functions they provide (Code)
  • Descriptions of data they can provide (Sensors)

Source http//www.darpa.mil/iso/DAML/
7
Example of searching on DAML-centric semantic Web
Source http//www.zdnet.com/pcweek/stories/jumps/
0,4270,2432946,00.html
8
The Power of Semantics
Semantics
Targeting
Directory
Table of Contents
Search
Index
Value of Information
9
Open Directory Project
10
Oingo.com
  • Oingo Ontology ODP based(?), the database of
    millions of concepts and relationships that
    powers Oingo's semantic technology
  • Oingo Seek - the database of millions of concepts
    and relationships that powers Oingo's semantic
    technology
  • Oingo Sense - the knowledge extraction tool that
    uncovers the essential meaning of information by
    sensing concepts and context
  • Oingo Lingua - the language of meaning used to
    state intent. The basis for intelligent
    interaction
  • Assets catalogued are Web sites or Web pages.

11
Test Query - "Tiger Woods"
12
Taalee
  • Taalee WorldModelTM Domain Models (metadata of
    domain-media-business attributes, types),
    Ontologies, Entities, Relationships, Automated
    Experts, Reference Data (Live Encyclopedia),
    Mappings
  • Taalee Distributed Intelligent Agent
    Infrastructure push/pull/scheduled agents for
    fresh extraction
  • Taalee Metabase of A/V assets
  • Taalee Semantic EngineTM with contextual reasoning

13
Taalee Semantic Services
Semantic Personalization
Semantic Cataloging
Semantic Search
Semantic Directory
Semantic Targeting
Semantic CategorIzation
Taalee Semantic Engine
Metabase Rapidly growing A/V aggregation
Automatic Extraction Agents Expert driven value
addition
WorldModel Understanding of content, profiles,
targeting needs
14
Semantic Cataloging
Taalee Metadata on Football Assets

 
 
 
Rich Media Reference Page
Baltimore 31, Pit 24
http//www.nfl.com
Quandry Ismail and Tony Banks hook up for their
third long touchdown, this time on a 76-yarder to
extend the Ravens lead to 31-24 in the third
quarter.
Professional Ravens, Steelers Bal 31, Pit
24 Quandry Ismail, Tony Banks Touchdown NFL.com 2/
02/2000
League Teams Score Players Event Produced
by Posted date
15
Metadata
What else can a context do? (a commercial
perspective)
Semantic Enrichment
16
Semantic Search
Simply the most precise and freshest A/V search
17
Creating a Web of related information
What can a context do?
18
Semantic Directory
System recognizes ENTITY CATEGORY
Relevant portion of the Directory is
automatically presented.
19
Semantic Directory
Users can explore Semantically related Information
.
20
Semantic Relationships
21
Looking ahead
22
Evolving targets and approaches in
integrating data and information (a personal
perspective)
23
  • Comprehensive knowledge-based, semantic
    information modeling, with multiple domain
    ontologies as a starting point, and
  • Distributed agents, to analyze Web-based
    content and establish/exploit semantic
    relationships.

24
  • Terminology (and language) transparency
  • Comprehensive metadata management
  • Context-sensitive information processing
  • Semantic correlation

25
Semantic Information Brokering
Information brokering is an architecture that
guides creation and management of information
systems and semantic-level solutions to serve a
variety of information stakeholders
(participants), including providers,
facilitators, consumers, and the business
involved in creating, enhancing and using of
information.
Kashyap Sheth 1993
26
Digital Earth Prototype System at UGA
  • Develop a Digital Earth Modeling System
  • Answer requests for collection of information
    from distributed resources
  • Develop a supportive learning environment for
    undergraduate geography students

27
Taking advantage of the Web for learning
28
An example scenario of learning on the Web
29
An example scenario of learning on the Web
? Adding on-the-fly user constraints while
processing the information request
Retrieve satellite images in 12-meter resolution
or higher, looking for soils with permeability
rate lt 10 (silty clay loam) for a new landfill
whose distance from the city industrial park is
less than 5km. Using the images coordinates,
forecast seismic activity up to moderate
magnitude (5 - 5.9, Richter scale) in the pointed
areas.
  • domain specific metadata
  • correlation among multiple ontologies
  • return results in multiple media (in this case,
    images and a simulation)

30
An example scenario of learning on the Web
Partial sample ontologies for semantic
information brokering
31
An example scenario of learning on the Web
A sample result (depending on information
providers) could be
images source http//www.orbimage.com
  • The students now have the information requested
    for helping the City Council in the
    planning of the new landfill

32
A Digital Library Scenario VOLCANOES ACTIVITY
33
A Digital Library Scenario VOLCANOES ACTIVITY
  • ? Some of the ontologies involved in
  • processing this information request are
  • Ontology for GIS Datasets
  • Ontology for Natural Disasters
  • Ontology for Volcanoes
  • Ontology for Landslides
  • Ontology for Tsunamis.

TRY HERE THIS AND OTHER CONCEPT DEMOS
34
Iscape working definition
35
Iscapes in the context of digital earth (ADEPT)
  • Iscapes are useful to understand geographical
    phenomena, typically involving relationships betwe
    en them
  • Iscapes are created by instructors using an
    iscape specification framework
  • Iscapes are run by students while learning about
    Digital Earth
  • Iscapes creation framework fits in the ADEPT
    agent -based architecture prototype

36
Iscape specification framework
Information Landscape
37
Information Landscapes
  • A modular specification framework to
    represent information landscapes
  • Specifications of complex information requests
    over multiple ontologies
  • Specification of relationships, including
    affects
  • Enabling user-configurable parameters
  • Enabling operations including simulations
  • A graphical toolkit for easy creation of iscapes

38
Information Landscapes
  • Learning paradigm for students
  • Uses embedded ontological terms and iscapes
  • Metadata framework
  • Models spatial, temporal and theme based
    metadata
  • Uses FGDC and Dublin Core standards to
    represent domain independent metadata

39
Example Ontology
40
Relations
  • Given a set X, a relation is some property
    that may or may not hold between one member
    of X and a member of another set
  • Various relationships equals,
    less_than, is_a, is_part_of, like

41
Semantic Relations
  • Most of these relations are hierarchical or
    similarity based
  • These are not powerful enough for our task of
    semantic interoperability between domains
    like Geography
  • In these domains, we have a natural
    affects relation between the ontologies

42
Semantic Relations
43
Design of affects
How do volcanoes affect the environment?
AFFECTS
44
Design of affects
Area (Pyroclastic Flows) INTERSECT Area (Crop)
gt Pyroclastic Flows destroy Crop Size (Ash
Particles) lt 2 gt Ash Rain cools the
Atmosphere Pyroclastic Flows destroy Crop and
Ash Rain cools the Atmosphere gt Volcanoes
affect the Environment (?x x?ASC) and (?y
y?BSC) FN(x) operator FN(y) gt ASC
relation BSC ASC relation BSC gt A affects
B
45
Mapping Functions
How do volcanoes affect the environment?
  • Location (Volcano) Location (Environment)
  • Enclosing function provides a standard
    interface to the operator
  • Operator does imprecise or fuzzy match
  • Achieves Geo-spatial interoperability

46
Mapping Functions
How do volcanoes affect the environment?
  • Time (Volcano) Time (Environment)
  • Matches, with a tolerance depending on the
    granularity of values
  • Tolerance different for different entities
    Specified default Can be user-defined
  • Achieves temporal interoperability

47
Realizing Semantic Information Brokering and
Semantic Web .conclusion
  • From Procedures, Objects, Components to Agents
    we have a nice abstraction of computation.
    Now lets apply them to address
    semantic-level issues
  • Semantic Web is a basis of handling information
    overload
  • Semantic Information Brokering gives a
    framework for enabling complex decision
    making and learning involving
    heterogeneous digital media on the Global
    Information Infrastructure

48
Further reading http//www.semanticweb.org
http//www.daml.org http//lsdis.cs.uga.edu/adep
t DAML could take search to a new level
http//www.zdnet.com/pcweek/stories/news/0,4153,24
32538,00.html V. Kashyap and A. Sheth,
Information Brokering, Kluwer Academic
Publishers, 2000 Tim Berners-Lee, Weaving the
Web, Harper, 1999. Editorial writing by Ramesh
Jain in IEEE Multimedia.
amit_at_taalee.com http//www.taalee.com ami
t_at_cs.uga.edu http//lsdis.cs.uga.edu
49
Backup/Detail Slides
  • For additional details on Information Brokering
    Architecture
  • Realizing Semantic Information Brokering and
    Semantic Web  ITC-IRST/University of Trento
    Seminar Series on  Perspectives on Agents
    Theories and Technologies,  April, 27, 2000,
    Trento, Italy
  • http//lsdis.cs.uga.edu/adept/presenta.html
  • For additional details on ISCAPE specification
    and Execution
  • Project Overview and Detailed Presentation at
  • http//lsdis.cs.uga.edu/adept/presenta.html
  • Demonstrations at
  • http//lsdis.cs.uga.edu/adept

50
Iscape specification using XML
lt! -- A template collection for all iscapes --
gt lt?xml version 1.0 ?gt lt!DOCYPE
IscapeCollection SYSTEM IscapeCollection.dtd
gt lt! -- All Iscapes -- gt ltIscapeCollectiongt lt!--
An iscape specification for how stratovolcanoes
affect the environment -- gt ltIscapegt lt --
Identifying this iscape -- gt ltIDgtVolcano Env
lt/IDgt ltNamegt How do stratovolcanoes affect the
environment lt/Namegt ltDescriptiongt An iscape
using the affects relationship
lt/Descriptiongt lt! All ontologies which
participate -- gt ltOntologiesgt ltOntologygtVolca
nolt/Ontologygt ltOntologygtEnvironmentlt/Ontologygt
lt/Ontologiesgt lt! Operations involved --
gt ltOperationgt ltRelationgtAffectslt/Relationgt
lt/Operationgt
51
Iscape specification using XML
lt! Constraints on ontologies -- gt ltOntological
Constraintsgt ltConstraintgt Volcano morphology is
stratovolcano lt/Constraintgt ltConstraintgt
Volcano start year is 1950 lt/Constraintgt lt/Ontolo
gical Constraintsgt lt!Metadata to present in the
result --gt ltPresentationgt Volcano and
Environment Metadata lt/Presentationgt lt!What
can the student configure -- gt ltStudentgt ltConf
iggt Location of Environment lt/Configgt lt/Studentgt
lt/Iscapegt lt!This Iscape Ends -- gt lt! Next
Iscape starts -- gt ltIscapegt lt/Iscapegt lt/Isca
peCollectiongt lt!Iscape Collection ends here --
gt
52
Relations
  • lt!-- Template collection of all relations in the
    system --gt
  • lt?xml version 1.0 gt
  • lt!DOCTYPE Relations SYSTEM Relations.dtd gt
  • ltRelationsgt
  • lt!--Relation specification starts here --gt
  • ltRelationgt
  • lt!-- Information to correlate with base iscape
    --gt
  • ltIscapeIDgt Volcano-Env lt/IscapeIDgt
  • ltNamegt Affects lt/Namegt
  • lt!-- Ontologies Involved --gt
  • ltOntologyAgt Volcano lt/OntologyAgt
  • ltOntologyBgt Environment lt/OntologyBgt
  • lt!-- All operators --gt
  • ltOperatorSetgt
  • lt!-- Specification has value and mapping
    conditions --gt
  • ltValueConditiongt
  • ltOntologyNamegt Environment lt/OntologyNamegt
  • ltAttributegt Damage lt/Attributegt
  • ltValOperatorgt GREATERTHANEQUALSlt/ValOperatorgt

53
Relations
  • ltMappingConditiongt
  • ltFunctionAgtArealt/FunctionAgt
  • ltElementAgtVolcanolt/FunctionAgt
  • ltOperatorgtEQUALSlt/Operatorgt
  • ltFunctionBgtArealt/Functiongt
  • ltElementBgtEnvironmentlt/ElementBgt
  • lt/MappingConditiongt
  • lt/OperatorSetgt
  • lt!-- End of all operators -- gt
  • lt/Relationgt
  • lt!-- End of this relation specification -- gt
  • lt/Relationsgt
  • lt!-- End of relation collection -- gt

54
Ontological Constraints
  • lt!-- Template to specify ontological constraints
    -- gt
  • lt?xml version 1.0 gt
  • lt!DOCTYPE OntologicalConstraints SYSTEM
    OntologicalConstraints.dtd gt
  • lt!-- A collection of ontological constraints for
    all iscapes -- gt
  • ltOntologicalConstraintsgt
  • lt -- A constraint on this iscape--gt
  • ltConstraintgt
  • ltIscapeIDgtVolcano-Envlt/IscapeIDgt
  • ltNamegtVolcano morphology is stratovolcanolt/Name
    gt
  • ltLHSOntologygtVolcanolt/LHSOntologygt
  • ltLHSAttributegtMorphologylt/LHSAttributegt
  • ltOperatorgtLIKElt/Operatorgt
  • ltTypegtStringlt/Typegt
  • ltRHSValuegtStratovolcanolt/RHSValuegt
  • lt/Constraintgt
  • lt/OntologicalConstraintsgt
  • lt! -- Collection of ontological constraints ends
    here -- gt

55
Presentation
  • lt!-- Template for presentation attributes - gt
  • lt?xml version 1.0 gt
  • lt!DOCTYPE Presentation SYSTEM Presentation.dtd
    gt
  • lt!-- All presentation attributes are embedded
    here - gt
  • ltPresentationgt
  • lt!-- presentation attributes for this iscape-- gt
  • ltIncludeThesegt
  • ltIscapeIDgtVolcano-Envlt/IscapeIDgt
  • ltNamegtVolcano and Environment Metadatalt/Namegt
  • ltIncludegt
  • ltOntologygtVolcanolt/Ontologygt
  • ltAttributegtTectonicSettinglt/Attributegt
  • lt/Includegt
  • ltIncludegt
  • ltOntologygtVolcanolt/Ontologygt
  • ltAttributegtEndYearlt/Attributegt
  • lt/Includegt
  • lt/IncludeThesegt
  • lt/Presentationgt

56
Student
  • lt !-- Template for student configurable
    attributes -- gt
  • lt! DOCTYPE Student SYSTEM Student.dtd gt
  • lt!-- All parameters which can be configured by a
    student -- gt
  • ltStudentgt
  • lt!-- Configuration for a particular iscape -- gt
  • ltConfiggt
  • lt!-- Correlating information -- gt
  • ltIscapeIDgtVolcano-Envlt/IscapeIDgt
  • ltNamegtLocation of environmentlt/Namegt
  • lt!-- The parameters which are configurable -- gt
  • ltParametergt
  • ltOntologygtEnvironmentlt/Ontologygt
  • ltAttributegtLocationNamelt/Attributegt
  • ltDisplayNamegt Configure Location lt/Displaygt
  • ltValuegt Hawaii lt/Valuegt
  • ltValuegt Kileauaea lt/Valuegt
  • lt/Parametergt
  • lt/Configgt
  • lt!-- Configuration for this iscape ends here -- gt

57
Operations
  • Powerful mechanism of studying geographical
    domains and other complex phenomena
  • Input parameters can be changed to support
    learning For e.g. statistical operations,
    numerical analysis simulation modeling, etc.

58
Clarkes Urban Growth Model (UGM)
Domain of Learning URBAN DYNAMICS
Demonstrates the utility of integrating existing
historic maps with remotely sensed data and
related geographic information to dynamically map
urban land characteristics for large metropolitan
areas.
San Francisco Bay Area prediction of urban extent
in 2100
59
Student interface
60
Digital Earth Prototype Project architecture
overview
61
The correlation agent
  • Receives the results collections from each
    of the resource agents
  • Correlates the results on basis of information
    provided in iscape and the query plan
    generated by planning agent
  • Performs data cleaning operations and merges
    the results into uniform result set and
    pass it on to user agent
  • Responsible for performing operations, if
    specified in the iscape

62
Realizing Semantic Information Brokering and
Semantic Web in summary
Popular Alternative perspective/approach
Linguistics, IR, AI
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