Semantic Web & Information Brokering: Opportunities, Commercialization and Challenges

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" VERSION="1.0" PREFIX=”PP" URL= "http://iwp.darpa.mil/ppt..html"> <CATEGORY NAME=”pp.presentation” FOR="http://iwp.darpa.mil/jhendler/agents.html"> <RELATION-VALUE POS1 = “Agents” POS2 = “/madhan”> <ONTOLOGY ID=”powerpoint-ontology" VERSION="1.0" DESCRIPTION=”formal model for powerpoint presentations"> <DEF-CATEGORY NAME=”Title" ISA=”Pres-Feature" > <DEF-CATEGORY NAME=”Subtitle" ISA=”Pres-Feature" > <DEF-RELATION NAME=”title-of" SHORT="was written by"> <DEF-ARG POS=1 TYPE=”presentation"> <DEF-ARG POS=2 TYPE=”presenter" > 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 - "Tiger Woods" 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 -- >

<?xml version = “1.0” >
<!DOCTYPE Relations SYSTEM “Relations.dtd” >
<Relations>

<Relation>

<IscapeID> Volcano-Env </IscapeID>
<Name> Affects </Name>

<OntologyA> Volcano </OntologyA>
<OntologyB> Environment </OntologyB>

<OperatorSet>

<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

<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 - >
<!DOCTYPE Presentation SYSTEM “Presentation.dtd” >
<!-- All presentation attributes are embedded here - >
<Presentation>
<!-- presentation attributes for this iscape-- >
<IncludeThese>
<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 -- >
<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

Semantic Web & Information Brokering: Opportunities, Commercialization and Challenges

by Amit Sheth

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