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Semantic Web & Information Brokering: Opportunities, Commercialization and Challenges

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Amit Sheth

The document discusses a workshop on the Semantic Web held in September 2000, outlining its models, architecture, and management. It emphasizes the importance of semantic annotations and interoperability to enhance web searches and content organization, while presenting various commercialization and research efforts. Additionally, it explores the integration of semantic information brokering and digital earth prototypes for practical applications in decision-making and education.

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Workshop on Semantic Web: Models, Architecture and Management September 21, 2000 – Lisbon, Portugal 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 Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges
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) Semantics: The Next step in the Web’s Evolution
“ 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. Semantic Web
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 Case Studies
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 allow semantic interoperability at the level we currently have syntactic interoperability in XML DARPA (and W3C) perspective DARPA Agent Mark Up Language (DAML) Program Manager: Professor James  Hendler  http://dtsn.darpa.mil/iso/programtemp.asp?mode=347
<Title> DAML <subtitle> an Example </subtitle> </title> <USE-ONTOLOGY ID=”PPT-ontology&quot; VERSION=&quot;1.0&quot; PREFIX=”PP&quot; URL= &quot;http://iwp.darpa.mil/ppt..html&quot;> <CATEGORY NAME=”pp.presentation” FOR=&quot;http://iwp.darpa.mil/jhendler/agents.html&quot;> <RELATION-VALUE POS1 = “Agents” POS2 = “/madhan”> <ONTOLOGY ID=”powerpoint-ontology&quot; VERSION=&quot;1.0&quot; DESCRIPTION=”formal model for powerpoint presentations&quot;> <DEF-CATEGORY NAME=”Title&quot; ISA=”Pres-Feature&quot; > <DEF-CATEGORY NAME=”Subtitle&quot; ISA=”Pres-Feature&quot; > <DEF-RELATION NAME=”title-of&quot; SHORT=&quot;was written by&quot;> <DEF-ARG POS=1 TYPE=”presentation&quot;> <DEF-ARG POS=2 TYPE=”presenter&quot; > Source : http://www.darpa.mil/iso/DAML/ DAML – an Example 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 Web Source: http://www.zdnet.com/pcweek/stories/jumps/0,4270,2432946,00.html
Value of Information Directory Targeting Search = Table of Contents = Index The Power of Semantics Semantics = Meaning with Context Semantics results in deep understanding of content, allowing highly relevant and fresh results, better personalization, and exceptional targeting.
Open Directory Project
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. Oingo.com
Test Query - &quot;Tiger Woods&quot; Broad taxonomy, Shallow understanding and results After 3 or 4 clicks
Taalee WorldModel TM: 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 Engine TM with contextual reasoning Taalee
Taalee Semantic Engine WorldModel: Understanding of content, profiles, targeting needs Automatic Extraction Agents: Expert driven value addition Metabase: Rapidly growing A/V aggregation Semantic Personalization Semantic Cataloging Semantic Search Semantic Targeting Semantic Directory Semantic CategorIzation Taalee Semantic Services WorldModel TM Extractor Agents Metabase
      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. 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: Semantic Cataloging Virage Search on football touchdown Jimmy Smith Interview Part Seven Jimmy Smith explains his philosophy on showboating. URL: http://cbs.sportsline... Brian Griese Interview Part Four Brian Griese talks about the first touchdown he ever threw. URL: http://cbs.sportsline... Metadata from Typical Cataloging of Football Assets
Metadata What else can a context do? (a commercial perspective) Semantic Enrichment
Simply the most precise and freshest A/V search Semantic Search Context and Domain Specific Attributes Uniform Metadata for Content from Multiple Sources, Can be sorted by any field Delightful, relevant information, exceptional targeting opportunity
Creating a Web of related information What can a context do?
System recognizes ENTITY & CATEGORY Relevant portion of the Directory is automatically presented. Semantic Directory
Users can explore Semantically related Information. Semantic Directory
Semantic Relationships
Looking ahead TO: Information requests Content search Semantic retrieval Interpretation Knowledge creation Knowledge sharing FROM: Browsing Lexical search Data exchange Data retrieval Semantic Information Brokering Semantic Web
Evolving targets and approaches in integrating data and information (a personal perspective) Mermaid DDTS Multibase, MRDSM, ADDS, IISS, Omnibase, ... Generation I (multidatabases) 1980s DL-II/DARPA/KA2 projects, OntoBroker, … Taalee, Observer ADEPT, InfoQuilt Generation III (information brokering) 1997... InfoSleuth, KMed, DL-I projects Infoscopes, HERMES, SIMS, Garlic,TSIMMIS,Harvest, RUFUS,... Generation II (mediators) 1990s VisualHarness InfoHarness Semantic Information Brokering Semantic Web
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. in a symbiotic approach Semantic Information Brokering Semantic Web
Terminology (and language) transparency Comprehensive metadata management Context-sensitive information processing Semantic correlation enablers of the emerging concepts Semantic Information Brokering Semantic Web
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. Semantic Information Brokering Kashyap & Sheth 1993
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
Taking advantage of the Web for learning Graduate students in a College of Geography have a final project in which a case of study is proposed. In the case, they are supposed to help a City Council in making decisions over the planning of a new landfill. This is a hands-on learning exercise through the interaction with a Digital Earth and the starting point would be to find the best location for the landfill*. Tacoma Landfill * This scenario comes in support of one of the suggestions for Digital Earth scenarios sampled by the “First Inter-Agency Digital Earth Working Group, an effort on behalf of NASA’s inter-agency Digital Earth Program.
An example scenario of learning on the Web by definition by semantics by synonymy  A first cut refinement leads us to the following information request: Find a proper soil in sites not subject to flooding or high groundwater levels for a new landfill near the industrial zone . Liquefaction phenomenon cannot occur . Find a landfill site for a new landfill near the source of the wastes . The earthquakes’ impacts must be evaluated .  A high level information request would be:
 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 < 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) An example scenario of learning on the Web
Partial sample ontologies for semantic information brokering: 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 An example scenario of learning on the Web OrbView-4’s stereo imaging capacity providing 3-D terrain images Hyperspectral data will be valuable for identifying material types 5km industrial zone identified landfill site
A Digital Library Scenario VOLCANOES ACTIVITY Some volcanoes are more active than others, and a few are in a state of permanent eruption, at least for the geological present. Volcanoes may become quiescent (dormant) for months or years. The danger to life posed by active volcanoes is not limited to eruption of molten rock or showers of ash and cinders. Mudflows that melt ice and snow on the volcano's flanks are equally hazardous*. * Encarta® 98 Desk Encyclopedia © & 1996-97 Microsoft Corporation.All rights reserved . Pu'u'O'o, Hawaii
 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 A Digital Library Scenario VOLCANOES ACTIVITY  A sample information request: Find information on volcanoes and also find how these volcanoes affect/cause landslides and tsunamis .
Iscape working definition “ An iscape is an information request that supports learning and semantic interoperability (about Digital Earth) “ (ADEPT at UGA)
Iscapes are useful to understand geographical phenomena, typically involving relationships between 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 Iscapes in the context of digital earth (ADEPT)
Iscape specification framework Information Landscape Ontologies Relationships Learning/What-if Operations/ Simulation Presentation Creation
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
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
Example Ontology NATURAL DISASTER Volcano Magnitude Range Damage in $ Damage Type Number of deaths Magnitude Flood Earthquake Tsunami
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”
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
Semantic Relations How does A affect B? A, in its entirety or by a set of its components, induces some changes or properties on a set of components of B
Design of “affects” How do volcanoes affect the environment? AFFECTS VOLCANO LOCATION ASH RAIN PYROCLASTIC FLOW ENVIRON. LOCATION PEOPLE ATMOSPHERE PLANT BUILDING DESTROYS COOLS TEMP DESTROYS KILLS
[Area (Pyroclastic Flows) INTERSECT Area (Crop)] => [Pyroclastic Flows d estroy Crop] [Size (Ash Particles) < 2] => [Ash Rain c ools the Atmosphere] [Pyroclastic Flows d estroy Crop] and [Ash Rain cools the Atmosphere] => [Volcanoes affect the Environment] (  x | x  ASC) and (  y | y  BSC) [ FN(x) operator FN(y) ]* => [ ASC relation BSC ] [ ASC relation BSC ]* => A affects B Design of “affects”
Mapping Functions [ Location (Volcano) = Location (Environment) ] Enclosing function provides a standard interface to the operator Operator does imprecise or fuzzy match Achieves Geo-spatial interoperability How do volcanoes affect the environment?
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 Brokering and Semantic Web …. conclusion
amit@taalee.com – http://www.taalee.com amit@cs.uga.edu – http://lsdis.cs.uga.edu 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. “ 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
For additional details on Information Brokering Architecture: Realizing Semantic Information Brokering and S emantic 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 Backup/Detail Slides
<! -- 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 -- >
<!-- 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 -- > Relations
<!-- 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 -- > Ontological Constraints
<!-- 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> <Ontology>Volcano</Ontology> <Attribute>TectonicSetting</Attribute> </Include> <Include> <Ontology>Volcano</Ontology> <Attribute>EndYear</Attribute> </Include> </IncludeThese> </Presentation> <!-- Presentation attributes end here -- > Presentation
< !-- 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 -- > Student
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
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 Brokering and Semantic Web in summary Popular Alternative perspective/approach: Linguistics, IR, AI 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

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Semantic Web & Information Brokering: Opportunities, Commercialization and Challenges

  • 1. Workshop on Semantic Web: Models, Architecture and Management September 21, 2000 – Lisbon, Portugal 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 Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges
  • 2. 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) Semantics: The Next step in the Web’s Evolution
  • 3. “ 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. Semantic Web
  • 4. 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 Case Studies
  • 5. 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 allow semantic interoperability at the level we currently have syntactic interoperability in XML DARPA (and W3C) perspective DARPA Agent Mark Up Language (DAML) Program Manager: Professor James  Hendler  http://dtsn.darpa.mil/iso/programtemp.asp?mode=347
  • 6. <Title> DAML <subtitle> an Example </subtitle> </title> <USE-ONTOLOGY ID=”PPT-ontology&quot; VERSION=&quot;1.0&quot; PREFIX=”PP&quot; URL= &quot;http://iwp.darpa.mil/ppt..html&quot;> <CATEGORY NAME=”pp.presentation” FOR=&quot;http://iwp.darpa.mil/jhendler/agents.html&quot;> <RELATION-VALUE POS1 = “Agents” POS2 = “/madhan”> <ONTOLOGY ID=”powerpoint-ontology&quot; VERSION=&quot;1.0&quot; DESCRIPTION=”formal model for powerpoint presentations&quot;> <DEF-CATEGORY NAME=”Title&quot; ISA=”Pres-Feature&quot; > <DEF-CATEGORY NAME=”Subtitle&quot; ISA=”Pres-Feature&quot; > <DEF-RELATION NAME=”title-of&quot; SHORT=&quot;was written by&quot;> <DEF-ARG POS=1 TYPE=”presentation&quot;> <DEF-ARG POS=2 TYPE=”presenter&quot; > Source : http://www.darpa.mil/iso/DAML/ DAML – an Example 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)
  • 7. Example of searching on DAML-centric semantic Web Source: http://www.zdnet.com/pcweek/stories/jumps/0,4270,2432946,00.html
  • 8. Value of Information Directory Targeting Search = Table of Contents = Index The Power of Semantics Semantics = Meaning with Context Semantics results in deep understanding of content, allowing highly relevant and fresh results, better personalization, and exceptional targeting.
  • 10. 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. Oingo.com
  • 11. Test Query - &quot;Tiger Woods&quot; Broad taxonomy, Shallow understanding and results After 3 or 4 clicks
  • 12. Taalee WorldModel TM: 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 Engine TM with contextual reasoning Taalee
  • 13. Taalee Semantic Engine WorldModel: Understanding of content, profiles, targeting needs Automatic Extraction Agents: Expert driven value addition Metabase: Rapidly growing A/V aggregation Semantic Personalization Semantic Cataloging Semantic Search Semantic Targeting Semantic Directory Semantic CategorIzation Taalee Semantic Services WorldModel TM Extractor Agents Metabase
  • 14.       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. 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: Semantic Cataloging Virage Search on football touchdown Jimmy Smith Interview Part Seven Jimmy Smith explains his philosophy on showboating. URL: http://cbs.sportsline... Brian Griese Interview Part Four Brian Griese talks about the first touchdown he ever threw. URL: http://cbs.sportsline... Metadata from Typical Cataloging of Football Assets
  • 15. Metadata What else can a context do? (a commercial perspective) Semantic Enrichment
  • 16. Simply the most precise and freshest A/V search Semantic Search Context and Domain Specific Attributes Uniform Metadata for Content from Multiple Sources, Can be sorted by any field Delightful, relevant information, exceptional targeting opportunity
  • 17. Creating a Web of related information What can a context do?
  • 18. System recognizes ENTITY & CATEGORY Relevant portion of the Directory is automatically presented. Semantic Directory
  • 19. Users can explore Semantically related Information. Semantic Directory
  • 21. Looking ahead TO: Information requests Content search Semantic retrieval Interpretation Knowledge creation Knowledge sharing FROM: Browsing Lexical search Data exchange Data retrieval Semantic Information Brokering Semantic Web
  • 22. Evolving targets and approaches in integrating data and information (a personal perspective) Mermaid DDTS Multibase, MRDSM, ADDS, IISS, Omnibase, ... Generation I (multidatabases) 1980s DL-II/DARPA/KA2 projects, OntoBroker, … Taalee, Observer ADEPT, InfoQuilt Generation III (information brokering) 1997... InfoSleuth, KMed, DL-I projects Infoscopes, HERMES, SIMS, Garlic,TSIMMIS,Harvest, RUFUS,... Generation II (mediators) 1990s VisualHarness InfoHarness Semantic Information Brokering Semantic Web
  • 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. in a symbiotic approach Semantic Information Brokering Semantic Web
  • 24. Terminology (and language) transparency Comprehensive metadata management Context-sensitive information processing Semantic correlation enablers of the emerging concepts Semantic Information Brokering Semantic Web
  • 25. 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. Semantic Information Brokering 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 Graduate students in a College of Geography have a final project in which a case of study is proposed. In the case, they are supposed to help a City Council in making decisions over the planning of a new landfill. This is a hands-on learning exercise through the interaction with a Digital Earth and the starting point would be to find the best location for the landfill*. Tacoma Landfill * This scenario comes in support of one of the suggestions for Digital Earth scenarios sampled by the “First Inter-Agency Digital Earth Working Group, an effort on behalf of NASA’s inter-agency Digital Earth Program.
  • 28. An example scenario of learning on the Web by definition by semantics by synonymy  A first cut refinement leads us to the following information request: Find a proper soil in sites not subject to flooding or high groundwater levels for a new landfill near the industrial zone . Liquefaction phenomenon cannot occur . Find a landfill site for a new landfill near the source of the wastes . The earthquakes’ impacts must be evaluated .  A high level information request would be:
  • 29. 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 < 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) An example scenario of learning on the Web
  • 30. Partial sample ontologies for semantic information brokering: An example scenario of learning on the Web
  • 31. 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 An example scenario of learning on the Web OrbView-4’s stereo imaging capacity providing 3-D terrain images Hyperspectral data will be valuable for identifying material types 5km industrial zone identified landfill site
  • 32. A Digital Library Scenario VOLCANOES ACTIVITY Some volcanoes are more active than others, and a few are in a state of permanent eruption, at least for the geological present. Volcanoes may become quiescent (dormant) for months or years. The danger to life posed by active volcanoes is not limited to eruption of molten rock or showers of ash and cinders. Mudflows that melt ice and snow on the volcano's flanks are equally hazardous*. * Encarta® 98 Desk Encyclopedia © & 1996-97 Microsoft Corporation.All rights reserved . Pu'u'O'o, Hawaii
  • 33. 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 A Digital Library Scenario VOLCANOES ACTIVITY  A sample information request: Find information on volcanoes and also find how these volcanoes affect/cause landslides and tsunamis .
  • 34. Iscape working definition “ An iscape is an information request that supports learning and semantic interoperability (about Digital Earth) “ (ADEPT at UGA)
  • 35. Iscapes are useful to understand geographical phenomena, typically involving relationships between 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 Iscapes in the context of digital earth (ADEPT)
  • 36. Iscape specification framework Information Landscape Ontologies Relationships Learning/What-if Operations/ Simulation Presentation Creation
  • 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 NATURAL DISASTER Volcano Magnitude Range Damage in $ Damage Type Number of deaths Magnitude Flood Earthquake Tsunami
  • 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 How does A affect B? A, in its entirety or by a set of its components, induces some changes or properties on a set of components of B
  • 43. Design of “affects” How do volcanoes affect the environment? AFFECTS VOLCANO LOCATION ASH RAIN PYROCLASTIC FLOW ENVIRON. LOCATION PEOPLE ATMOSPHERE PLANT BUILDING DESTROYS COOLS TEMP DESTROYS KILLS
  • 44. [Area (Pyroclastic Flows) INTERSECT Area (Crop)] => [Pyroclastic Flows d estroy Crop] [Size (Ash Particles) < 2] => [Ash Rain c ools the Atmosphere] [Pyroclastic Flows d estroy Crop] and [Ash Rain cools the Atmosphere] => [Volcanoes affect the Environment] (  x | x  ASC) and (  y | y  BSC) [ FN(x) operator FN(y) ]* => [ ASC relation BSC ] [ ASC relation BSC ]* => A affects B Design of “affects”
  • 45. Mapping Functions [ Location (Volcano) = Location (Environment) ] Enclosing function provides a standard interface to the operator Operator does imprecise or fuzzy match Achieves Geo-spatial interoperability How do volcanoes affect the environment?
  • 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. 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 Brokering and Semantic Web …. conclusion
  • 48. [email protected] – http://www.taalee.com [email protected] – http://lsdis.cs.uga.edu 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. “ 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
  • 49. For additional details on Information Brokering Architecture: Realizing Semantic Information Brokering and S emantic 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 Backup/Detail Slides
  • 50. <! -- 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
  • 51. 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 -- >
  • 52. <!-- 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
  • 53. <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 -- > Relations
  • 54. <!-- 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 -- > Ontological Constraints
  • 55. <!-- 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> <Ontology>Volcano</Ontology> <Attribute>TectonicSetting</Attribute> </Include> <Include> <Ontology>Volcano</Ontology> <Attribute>EndYear</Attribute> </Include> </IncludeThese> </Presentation> <!-- Presentation attributes end here -- > Presentation
  • 56. < !-- 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 -- > Student
  • 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. 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
  • 60. Digital Earth Prototype Project: architecture overview
  • 61. 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
  • 62. Realizing Semantic Information Brokering and Semantic Web in summary Popular Alternative perspective/approach: Linguistics, IR, AI 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

Editor's Notes

  • #35: This is a more formal definition of an iscape. W e say “distributed” because the information to answer the request can lie in different sources.
  • #36: In the context of digital earth , iscapes serve a very important role. We have developed a framework to specify iscapes declaratively. The primary usage of iscapes is meant by students. Iscapes serves as a ideal platform for students to lean about phenomena as the the requests are preformulated by the administrator and all students need to do is to select parameters and click on the request.
  • #37: This is the specification framework for an iscape. An iscape can basically consist of 6 components as described below Ontologies serve as the shared vocabulary. Relationships serve as the smantic correlation layer. We could use simulation to demonstrate a concept graphically. Ontology Constraints are constraints that we can specify on the ontologies involved in the iscape. Iscapes can yield a lot of metadata. The presentation serves to filter the metadata. Finally one of the most important components is the student component where a student can configure parameters and learn interactively from the iscape.
  • #40: This is an example ontology developed for the geographic domain. We shall get back to this topic later.
  • #41: For e.g., x &lt; y is a relationship that may hold between x and y. We have come across relations like…”equals”, “less_than”, “is_a”, etc
  • #42: Most of these relations are not powerful enough to correlate complex entities in many common (and natural) domain like geography.
  • #44: Now, lets take an example to see how we design the “affects” relationship. We see that different components of a volcano can affect different components of the environment. Put together, they can describe completely how volcanoes affect the environment. In this case, lets look at a few example components that affect others. Pyroclastic flows, if they flow across crops, will destroy them. So, we can say, if area of Pyroclastic flows intersect the area of crops, Pyroclastic flows destroy crops. Also, if the ash particles strewn from the volcano disperse into the atmosphere as tiny particles, their size can determine if they have a cooling effect on the atmosphere.
  • #45: Let us put these sub-relations down into words. We see here that all these follow a specific pattern. [Function (something) operator Function (something else)]. If we generalize this, we can see that FN(x) op FN(y) where x and y are sub-components of A and B ontologies respectively. This schema can be used to define relationships in any domain and examples in 6-7 domains are shown in the thesis report.
  • #46: Let us first take the case of comparison of locations. When we say location of volcano = location of the environment, we don’t expect to match the exact point of the volcano and the location specified. In general, the volcano’s effects would be felt around a certain area surrounding the volcano. We model this by including a tolerance level within which we match the location points. In this way, we perform a sort of imprecise of fuzzy match and helps us remove geo-spatial inconsistencies. This mapping technique is standardized by the use of enclosing functions and overloading the operator. We have developed mapping functions for the geographic domain and we need only to plug-in any function if we need other functionality.
  • #47: This is an example of temporal matching. We can find out whether the given volcano had an affect on the environment on the given date. We know that a volcano’s effects like lava flows, etc would continue for a couple of days. We can assume this as tolerance. If the given date falls within this tolerance, we return a successful match. In the case of an earthquake, the time period is in the range of minutes.
  • #51: .All iscapes and their components are specified using the Extendible Markup Language. Every iscape has an id , name and description. The ontologies involved and the name of the remaining components are then embedded in the iscape.
  • #52: For example, if the iscape administrator wanted tp specify that the volcano was a stratovolcano, he could specify the name of the constraints within the constraint tags.
  • #53: Relations have mapping conditions and value conditions. Mapping conditions are functions that you could apply on ontological terms , for example the area function equates the bounding coordinates of two ontologies. Value conditions denote configurable relationship parameters.
  • #55: This component specifies the actual constraint. Here , we see that the iscape id and constraint name are the same as in the base iscape . This is then followed by the actual constraint specfication.
  • #56: We can see that several metadata attributes can be included in the result presentation. The presentation layer is needed as we can then filter out the metadata returned by the system as result.
  • #57: In this component , we encode what parameters can be configured in the iscape. Here we see that , the location of the environment ontology can be configured and the values that this parameter can take are Hawaii and Kileau.
  • #58: 1. Operations are important for the ADEPT system as they lend themselves easily for changing parameters and viewing different results for every set of parameters which are entered by the user. 2. Geography instructors use a lot of simulation models to explain various concepts of geography to their students.
  • #59: 1. A cellular automaton model of urban growth 2. Urbanization, agricultural intensification, resource extraction, and water resources development are examples of human-induced phenomena that have significant impact on people, economy and resources 3. Based on an understanding of the land use changes, it may be possible to understand the impacts associated with them and contribute to a productive national environmental sustainability
  • #60: This screen shows the student interface to ADEPT. We can see that the ontologies, volcanoes and environment are used here as well as the ontology country. All ontological terms and iscapes along with configurable parameters are embedded in the same screen.