Semantic Web & Information Brokering: Opportunities, Commercialization and Challenges
•Transferir como PPT, PDF•
5 gostaram•2,994 visualizações
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).
![[object Object],[object Object],[object Object],Semantics: The Next step in the Web’s Evolution](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recomendados
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (20)
Semelhante a Semantic Web & Information Brokering: Opportunities, Commercialization and Challenges
Semelhante a Semantic Web & Information Brokering: Opportunities, Commercialization and Challenges (20)
Último
Último (20)
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
- 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.
- 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
- 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
- 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
Notas do Editor
- 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.
- 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.
- 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.
- This is an example ontology developed for the geographic domain. We shall get back to this topic later.
- For e.g., x < y is a relationship that may hold between x and y. We have come across relations like…”equals”, “less_than”, “is_a”, etc
- Most of these relations are not powerful enough to correlate complex entities in many common (and natural) domain like geography.
- 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.
- 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.
- 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.
- 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.
- .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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.