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).

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

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.

9. Open Directory Project

11. Test Query - "Tiger Woods" Broad taxonomy, Shallow understanding and results After 3 or 4 clicks

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

20. Semantic Relationships

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

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

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:

30. Partial sample ontologies for semantic information brokering: An example scenario of learning on the Web

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

36. Iscape specification framework Information Landscape Ontologies Relationships Learning/What-if Operations/ Simulation Presentation Creation

39. Example Ontology NATURAL DISASTER Volcano Magnitude Range Damage in $ Damage Type Number of deaths Magnitude Flood Earthquake Tsunami

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”

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 -- >

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

59. Student interface

60. Digital Earth Prototype Project: architecture overview

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