Workshop on Semantic Web: Models, Architecture and Management September 21, 2000 – Lisbon, Portugal Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges 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 Special thanks, Digital Library project team at LSDIS
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Semantic Web & Information Brokering: Opportunities, Commercialization and Challenges
Amit Sheth, "Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges," Keynote talk at the workshop on Semantic Web: Models, Architecture and Management, September 21, 2000, Lisbon, Portugal.
This was the keynote given at probably the first international event with "Semantic Web" in title (and before the well known SciAm article). As in TBL's use of Semantic Web in his 1999 book, (semantic) metadata plays central role. The use of Worldmodel/Ontology is consistent with our use of ontology for (Web) information integration in 1994 CIKM paper. Summary of the talk by event organizers and other details are at: http://knoesis.org/library/resource.php?id=735
Prof. Sheth started a Semantic Web company Taalee, Inc. in 1999 (product was called MediaAnywhere A/V search engine- discussed in this paper in the context of one of its use by a customer Redband Broadcasting). The product included Semantic Web/populated Ontology based semantic (faceted) search, semantic browsing, semantic personalization, semantic targeting (advertisement), etc as is described in U.S. Patent #6311194, 30 Oct. 2001 (filed 2000). MediaAnywhere has about 25 ontologies in News/Business, Sports, Entertainment, etc.
Taalee merged to become Voquette in 2001 (product was called SCORE), Semagix in 2004 (product was called Semagix Freedom), and then Fortent in 2006 (products included Know Your Customers).
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Transcript
Workshop on Semantic Web: Models, Architecture and Management
September 21, 2000 – Lisbon, Portugal
Semantic Web & Info. BrokeringOpportunities, Commercialization and Challenges
byAmit 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
Special thanks, Digital Library project team at LSDIS
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)
• “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.
• 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
1. Create an Agent Mark-Up Language (DAML) built upon XML that allows users to provide machine-readable semantic annotations for specific communities of interest.
2. 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.
3. Use these tools to build up, instantiate, operate, and test sets of agent-based programs that markup and use DAML.
4. 5. 6. ….applications
allow semantic interoperability at the level we currently have syntactic interoperability in XML
DARPA Agent Mark Up Language (DAML)Program Manager: Professor James Hendler http://dtsn.darpa.mil/iso/programtemp.asp?mode=347
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)
Example of searching on DAML-centric semantic WebExample of searching on DAML-centric semantic Web
Sou
rce
: ht
tp:/
/ww
w.z
dne
t.co
m/p
cwee
k/st
orie
s/ju
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/0,4
270,
2432
946
,00.
htm
l
Value of Information
Directory
Tar
get
ing
Search= Table of Contents
= Index
= Meaning with Context
Semantics results in deep understanding of content, allowing highly relevant and fresh results, better personalization,
and exceptional targeting.
Semantics
• 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.
Broad taxonomy,Shallow understanding and results
After 3 or 4 clicks
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
Taalee Semantic Engine
WorldModelTM
Extractor Agents
WorldModel: Understanding of content, profiles, targeting needs
Automatic Extraction Agents: Expert driven value addition
Metabase
Metabase: Rapidly growing A/V aggregation
SemanticPersonalization
Semantic Cataloging
SemanticSearch
SemanticTargeting
SemanticDirectory
Semantic CategorIzation
Virage Search on football touchdown
Jimmy Smith Interview Part SevenJimmy Smith explains his philosophy on showboating. URL: http://cbs.sportsline...
Brian Griese Interview Part FourBrian Griese talks about the first touchdown he ever threw. URL: http://cbs.sportsline...
Metadata from Typical Cataloging of Football
Assets
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 Raven’s lead to 31-24 in the third quarter.
ProfessionalRavens, SteelersBal 31, Pit 24Quandry Ismail, Tony BanksTouchdownNFL.com2/02/2000
League:Teams:Score:
Players:Event:
Produced by:Posted date:
Wh
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can
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text
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?(a
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al p
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ve)
Sem
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chm
ent
Simply the most precise and freshest A/V search
Context and Domain Specific Attributes Uniform Metadata for Content from Multiple Sources, Can be sorted by any field
Enclosing function provides a standard interface to the operator
Operator does imprecise or fuzzy match
Achieves Geo-spatial interoperability
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
From Procedures, Objects, Components to Agents
we have a nice abstraction of computation. Now
let’s 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
Realizing Semantic Information Brokeringand Semantic Web ….conclusion
“Humankind has not woven the web of life.We are but one thread within it.Whatever we do to the web, we do to ourselves.All things connect.”– Chief Seattle, 1854
Further reading http://www.semanticweb.org http://www.daml.org http://lsdis.cs.uga.edu/~adept “DAML could take search to a new level” http://www.zdnet.com/pcweek/stories/news/0,4153,2432538,00.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.
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
<! -- A template collection for all iscapes -- >
<?xml version = “1.0” ?>
<!DOCYPE IscapeCollection SYSTEM “IscapeCollection.dtd” >
<! -- All Iscapes -- >
<IscapeCollection>
<!-- An iscape specification for how stratovolcanoes affect the environment -- >
<Iscape>
< -- Identifying this iscape -- >
<ID>Volcano – Env </ID>
<Name> How do stratovolcanoes affect the environment </Name>
<Description> An iscape using the affects relationship </Description>
<! – All ontologies which participate -- >
<Ontologies>
<Ontology>Volcano</Ontology>
<Ontology>Environment</Ontology>
</Ontologies>
<! – Operations involved -- >
<Operation>
<Relation>Affects</Relation>
</Operation>
Iscape specification using XML
Iscape specification using XML <!— Constraints on ontologies -- >
<Ontological Constraints>
<Constraint> Volcano morphology is stratovolcano </Constraint>
<Constraint> Volcano start year is 1950 </Constraint>
</Ontological Constraints>
<!—Metadata to present in the result -->
<Presentation> Volcano and Environment Metadata </Presentation>
<!—What can the student configure -- >
<Student>
<Config> Location of Environment </Config>
</Student>
</Iscape>
<!—This Iscape Ends -- >
<! – Next Iscape starts -- >
<Iscape>
…
…
</Iscape>
</IscapeCollection>
<!—Iscape Collection ends here -- >
Relations <!-- Template collection of all relations in the system -->
<?xml version = “1.0” >
<!DOCTYPE Relations SYSTEM “Relations.dtd” >
<Relations>
<!--Relation specification starts here -->
<Relation>
<!-- Information to correlate with base iscape -->
<IscapeID> Volcano-Env </IscapeID>
<Name> Affects </Name>
<!-- Ontologies Involved -->
<OntologyA> Volcano </OntologyA>
<OntologyB> Environment </OntologyB>
<!-- All operators -->
<OperatorSet>
<!-- Specification has value and mapping conditions -->
<ValueCondition>
<OntologyName> Environment </OntologyName>
<Attribute> Damage </Attribute>
<ValOperator> GREATERTHANEQUALS</ValOperator>
<Value> 10000 </Value>
<Type> Integer </Type>
</ValueCondition>
Relations
<MappingCondition>
<FunctionA>Area</FunctionA>
<ElementA>Volcano</FunctionA>
<Operator>EQUALS</Operator>
<FunctionB>Area</Function>
<ElementB>Environment</ElementB>
</MappingCondition>
</OperatorSet>
<!-- End of all operators -- >
</Relation>
<!-- End of this relation specification -- >
</Relations>
<!-- End of relation collection -- >
Ontological Constraints <!-- Template to specify ontological constraints -- >
<?xml version = “1.0” >
<!DOCTYPE OntologicalConstraints SYSTEM “OntologicalConstraints.dtd” >
<!-- A collection of ontological constraints for all iscapes -- >
<OntologicalConstraints>
< -- A constraint on this iscape-->
<Constraint>
<IscapeID>Volcano-Env</IscapeID>
<Name>Volcano morphology is stratovolcano</Name>
<LHSOntology>Volcano</LHSOntology>
<LHSAttribute>Morphology</LHSAttribute>
<Operator>LIKE</Operator>
<Type>String</Type>
<RHSValue>Stratovolcano</RHSValue>
</Constraint>
</OntologicalConstraints>
<! -- Collection of ontological constraints ends here -- >
Presentation <!-- Template for presentation attributes - ><?xml version = “1.0” ><!DOCTYPE Presentation SYSTEM “Presentation.dtd” ><!-- All presentation attributes are embedded here - ><Presentation> <!-- presentation attributes for this iscape-- ><IncludeThese>
<IscapeID>Volcano-Env</IscapeID><Name>Volcano and Environment Metadata</Name><Include>
</Include></IncludeThese></Presentation><!-- Presentation attributes end here -- >
Student < !-- Template for student configurable attributes -- >
<! DOCTYPE Student SYSTEM “Student.dtd” >
<!-- All parameters which can be configured by a student -- >
<Student>
<!-- Configuration for a particular iscape -- >
<Config>
<!-- Correlating information -- >
<IscapeID>Volcano-Env</IscapeID>
<Name>Location of environment</Name>
<!-- The parameters which are configurable -- >
<Parameter>
<Ontology>Environment</Ontology>
<Attribute>LocationName</Attribute>
<DisplayName> Configure Location </Display>
<Value> Hawaii </Value>
<Value> Kileauaea </Value>
</Parameter>
</Config>
<!-- Configuration for this iscape ends here -- >
</Student>
<!-- End of all student configurable parameters -- >
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.
Clarke’s Urban Growth Model (UGM)
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
Domain of Learning – URBAN DYNAMICS
Student interface
Digital Earth Prototype Project: architecture overview
RELATE
CorrelationAgent
PlanningAgent
User Agent
WrappedResource
Agent
OntologyAgent
Broker
CostModel
Web Wrapper
SimulationDatabaseWrapper
ADEPTMetabase
MetabaseResource
Agent
SimulationResource
Agent
RELATE
CorrelationAgent
PlanningAgent
User Agent
WrappedResource
Agent
OntologyAgent
Broker
CostModel
Web Wrapper
SimulationDatabaseWrapper
ADEPTMetabase
MetabaseResource
Agent
SimulationResource
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
The correlation agent
Realizing Semantic Information Brokeringand Semantic Web in summary