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Knowledge SifterKnowledge Sifter: : Agent-Based Search over Agent-Based Search over Heterogeneous Sources Heterogeneous Sources using Semantic Web using Semantic Web

ServicesServicesFaculty:Faculty: Dr. Larry Kerschberg and Dr. Daniel Dr. Larry Kerschberg and Dr. Daniel

MenascéMenascéStudents:Students: Hanjo Jeong, Scott Mitchell and Ahmed Hanjo Jeong, Scott Mitchell and Ahmed

Abu JbaraAbu JbaraAffiliates:Affiliates: Drs. Riki Morikawa, Randy Howard, & Drs. Riki Morikawa, Randy Howard, &

Wooju KimWooju KimE-Center for E-Business, E-Center for E-Business, http://eceb.gmu.eduhttp://eceb.gmu.edu/ / Volgenau School of Information Technology and Volgenau School of Information Technology and

EngineeringEngineeringGeorge Mason University, Fairfax, VirginiaGeorge Mason University, Fairfax, Virginia

Sponsored by the NGA: National Geospatial-Sponsored by the NGA: National Geospatial-Intelligence AgencyIntelligence Agency

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Presentation OutlinePresentation Outline

Goals of the Knowledge Sifter Project;Goals of the Knowledge Sifter Project; Knowledge Sifter Architecture for semantic Knowledge Sifter Architecture for semantic

querying, accessing, ranking and querying, accessing, ranking and integrating information from heterogeneous integrating information from heterogeneous data sources;data sources;

Specification and design of Knowledge Specification and design of Knowledge Sifter Meta-Model for storing end-to-end Sifter Meta-Model for storing end-to-end scenario information in a Knowledge scenario information in a Knowledge Repository;Repository;

ConclusionsConclusions Demonstration of Knowledge Sifter 2 Demonstration of Knowledge Sifter 2

Prototype.Prototype.

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Goals of Knowledge Sifter Goals of Knowledge Sifter Project - 1Project - 1

To provide intelligence analysts with To provide intelligence analysts with services to: services to: Specify queries related to their work tasks Specify queries related to their work tasks and and

Retrieve, rank and integrate results from Retrieve, rank and integrate results from multiple sources;multiple sources;

To use the emerging Semantic Web to To use the emerging Semantic Web to create an object-oriented view of create an object-oriented view of people, places, things and events by people, places, things and events by aggregating and integrating information aggregating and integrating information from multiple heterogeneous sources.from multiple heterogeneous sources.

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Goals of Knowledge Sifter Goals of Knowledge Sifter Project - 2Project - 2

To use open standards to easily incorporate To use open standards to easily incorporate new ontologies and sources in a plug-and-new ontologies and sources in a plug-and-play fashion:play fashion: Imagery Standards (Web Map Services and Web Feature Imagery Standards (Web Map Services and Web Feature Services, and ISO Standards 19115 and 19139);Services, and ISO Standards 19115 and 19139);

Semantic Web (RDF, RDFS, Web Ontology Language – Semantic Web (RDF, RDFS, Web Ontology Language – OWL)OWL)

Web Services and Semantic Web Services to allow Web Services and Semantic Web Services to allow sources to be easily discovered and incorporated sources to be easily discovered and incorporated into the Knowledge Sifter architecture.into the Knowledge Sifter architecture.

To create an agent-based service-oriented To create an agent-based service-oriented architecture that takes user queries, architecture that takes user queries, enhances them semantically, submits them for enhances them semantically, submits them for processing against multiple heterogeneous processing against multiple heterogeneous sources, and ranks them according to the sources, and ranks them according to the user’s preferences and the systems user’s preferences and the systems similarity metrics;similarity metrics;

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Goals of Knowledge Sifter Goals of Knowledge Sifter Project - 3Project - 3

Monitor and capture the actions and Monitor and capture the actions and artifacts of users, KS agents, and data artifacts of users, KS agents, and data sources so as to learn user patterns, sources so as to learn user patterns, system patterns and source patterns in system patterns and source patterns in order to order to evolveevolve Knowledge Sifter over time. Knowledge Sifter over time.

To use data mining techniques on the To use data mining techniques on the knowledge repository to mine patterns such knowledge repository to mine patterns such as:as: User preferences, contexts, and biases;User preferences, contexts, and biases; System templates for Web services choreography; System templates for Web services choreography; Data source QoS, availability and Data source QoS, availability and authoritativeness. authoritativeness.

Monitor sources in real-time for QoS and Monitor sources in real-time for QoS and adjust query traffic using a Web services adjust query traffic using a Web services broker.broker.

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Knowledge Sifter Knowledge Sifter ArchitectureArchitecture Three-layer Three-layer

architecture: architecture: User, Knowledge User, Knowledge Management, and Management, and Data Sources,Data Sources,

Autonomous Autonomous agents handle agents handle specialized specialized tasks,tasks,

Multiple domain Multiple domain models, models, ontologies, and ontologies, and authoritative authoritative services;services;

Web services Web services agent handles agent handles requests to requests to multiple multiple heterogeneous heterogeneous sources;sources;

Ranking agent Ranking agent rates results rates results based on user based on user and system and system preferences.preferences.

Data Sources Layer

Knowledge Management Layer

User Layer

WebServices

Agent

User Agent

WordNet

YahooImages

GoogleEarth

GoogleMaps

Geospatial Features

GNIS GEONet

TerraServer

WMS

NASA USGS

Preferences Agent

RankingAgent

QueryFormulation

Agent

OntologyAgent

LegendAgent Interaction

Data Flow

Ontology Repository

FeatureOntology

SpatialOntology

TemporalOntology

ImageryDomainModel

Data Sources Layer

Knowledge Management Layer

User Layer

WebServices

Agent

User Agent

WordNet

YahooImages

GoogleEarth

GoogleMaps

Geospatial Features

GNIS GEONet

Geospatial Features

GNIS GEONet

TerraServer

WMS

NASA USGS

WMS

NASA USGS

Preferences Agent

RankingAgent

QueryFormulation

Agent

OntologyAgent

LegendAgent Interaction

Data Flow

LegendAgent Interaction

Data Flow

Ontology Repository

FeatureOntology

SpatialOntology

TemporalOntology

ImageryDomainModel

Ontology Repository

FeatureOntology

SpatialOntology

TemporalOntology

ImageryDomainModel

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Imagery Domain Model in UMLImagery Domain Model in UML Imagery Domain Model is Imagery Domain Model is

the image ontology;the image ontology; An Image has several An Image has several

Features such as Date Features such as Date and Size, with their and Size, with their respective attributes. respective attributes.

An Image has a Source An Image has a Source and contains Content and contains Content such as a Person, such as a Person, Thing, or Place.Thing, or Place.

Classes are related by Classes are related by relationships and ISA relationships and ISA relationships.relationships.

Classes have Classes have properties.properties.

OWL schema of Imagery OWL schema of Imagery Domain Model used by Domain Model used by Knowledge Sifter agents Knowledge Sifter agents to instantiate a query to instantiate a query and associated and associated metadata.metadata.

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User LayerUser Layer User Agent interacts with user to User Agent interacts with user to obtain information regarding obtain information regarding query specification;query specification;

Cooperates with Preference Agent Cooperates with Preference Agent to provide personalized criteria to provide personalized criteria for search preferences, for search preferences, authoritative sites, and result authoritative sites, and result ranking evaluation rules;ranking evaluation rules;

Cooperates with Query Formulation Cooperates with Query Formulation Agent to convey user preferences Agent to convey user preferences and the user’s initial query.and the user’s initial query.

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Knowledge Management Knowledge Management LayerLayer

Query Formulation Agent consults the Query Formulation Agent consults the Ontology Agent to enhance the query Ontology Agent to enhance the query with “semantic” concepts.with “semantic” concepts.

Ontology Agent uses Imagery Domain Ontology Agent uses Imagery Domain Model, authoritative name services, Model, authoritative name services, and associated ontologies to specify and associated ontologies to specify semantic search concepts and semantic search concepts and coordinates for objects of interest.coordinates for objects of interest. Authoritative Name Services include Authoritative Name Services include WordNet from Princeton University, GNIS WordNet from Princeton University, GNIS from USGS, and GEONet from NGA.from USGS, and GEONet from NGA.

Query Formulation Agent receives the Query Formulation Agent receives the semantic query and passes it to the semantic query and passes it to the Web Services Agent for processing. Web Services Agent for processing.

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Knowledge Management Knowledge Management LayerLayer

Web Services AgentWeb Services Agent Decomposes the query into subqueries and determines Decomposes the query into subqueries and determines which Web Services or wrapped sources should process which Web Services or wrapped sources should process the sub-queries;the sub-queries;

Translates the subqueries into query format of Translates the subqueries into query format of local sources;local sources;

In the case of Web Services such as TerraServer, In the case of Web Services such as TerraServer, uses the SOAP message format specified by the WSDL;uses the SOAP message format specified by the WSDL;

Selects the appropriate data source with semantic Selects the appropriate data source with semantic quality, QoS, and availability factors;quality, QoS, and availability factors;

Handles the choreography of web services execution;Handles the choreography of web services execution; Results, returned to Web Services Agent, are then Results, returned to Web Services Agent, are then sent to the Ranking Agent.sent to the Ranking Agent.

Ranking AgentRanking Agent Ranks the resulting information according to Ranks the resulting information according to user ranking preferences, source user ranking preferences, source authoritativeness, similarity measures, etc.authoritativeness, similarity measures, etc.

Sends results to User Agent for presentation Sends results to User Agent for presentation to the user.to the user.

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Data Sources LayerData Sources Layer Web Services and wrappers used to Web Services and wrappers used to link to data sources;link to data sources;

Heterogeneous data sources include, Heterogeneous data sources include, Image metadata, image archives, XML-Image metadata, image archives, XML-repositories, relational databases, the repositories, relational databases, the Web and the emerging Semantic Web.Web and the emerging Semantic Web.

Quality of Service IssuesQuality of Service Issues Specification of performance and Specification of performance and availability QoS goals.availability QoS goals.

QoS negotiation protocols.QoS negotiation protocols. Hierarchical caching to support Hierarchical caching to support scalability.scalability.

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Knowledge Sifter Meta-Knowledge Sifter Meta-ModelModel

Meta-model Meta-model describes agent describes agent interaction, KS interaction, KS artifacts, artifacts, feedback by feedback by users, etc.users, etc.

Meta-model Meta-model serves as a serves as a schema for schema for capturing and capturing and storing storing artifacts such artifacts such as user queries, as user queries, reformulated reformulated queries, data queries, data sources used, sources used, query results, query results, ranked results, ranked results, user feedback, user feedback, etc.etc.

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Knowledge Sifter Meta-ModelKnowledge Sifter Meta-Model

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Protégé Meta-Model OntologyProtégé Meta-Model Ontology

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KS Meta-Model OWL KS Meta-Model OWL SpecificationSpecification

OWL & RDF OWL & RDF Specification Specification generated generated automatically from automatically from Protégé Protégé specification.specification.

Meta-model guides Meta-model guides the functioning of the functioning of Knowledge Sifter and Knowledge Sifter and captures the captures the relevant data from relevant data from the actual agent-the actual agent-based execution.based execution.

Data stored in MySQL Data stored in MySQL database according database according to a relational to a relational database for meta-database for meta-model.model.

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ConclusionsConclusions The goals of Knowledge Sifter are to provide The goals of Knowledge Sifter are to provide

services for analysts to pose semantic queries services for analysts to pose semantic queries to multiple heterogeneous sources without to multiple heterogeneous sources without regard to the format or location of those regard to the format or location of those resources.resources.

KS is based on open standards – Imagery, KS is based on open standards – Imagery, Semantic Web and Web Services – allowing a Semantic Web and Web Services – allowing a plug-and-play semantic architecture.plug-and-play semantic architecture.

KS uses authoritative name services to provide KS uses authoritative name services to provide concept synonyms (WordNet), and object location concept synonyms (WordNet), and object location services (GNIS and GNS).services (GNIS and GNS).

KS sources are accessed via Web service API KS sources are accessed via Web service API (TerraServer) or via wrappers.(TerraServer) or via wrappers.

Longer-term research will focus on:Longer-term research will focus on: Support for emergent semantics and evolution; Support for emergent semantics and evolution; Collaborative filtering to inform users when others Collaborative filtering to inform users when others are interested in similar concepts; andare interested in similar concepts; and

Mechanisms by which analysts may specify hypotheses Mechanisms by which analysts may specify hypotheses or scenarios, and the evidence will be drawn from the or scenarios, and the evidence will be drawn from the multiple heterogeneous sources.multiple heterogeneous sources.

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Knowledge Sifter PublicationsKnowledge Sifter Publicationshttp://eceb.gmu.edu/publicationhttp://eceb.gmu.edu/publications.htmls.html

L. Kerschberg, M. Chowdhury, A. Damiano, H. Jeong, S. L. Kerschberg, M. Chowdhury, A. Damiano, H. Jeong, S. Mitchell, J. Si, and S. Smith, “Knowledge Sifter: Agent-Mitchell, J. Si, and S. Smith, “Knowledge Sifter: Agent-Based Ontology-Driven Search over Heterogeneous Databases Based Ontology-Driven Search over Heterogeneous Databases using Semantic Web Services,” in Semantics for a Networked using Semantic Web Services,” in Semantics for a Networked World, Semantics for the Grid Databases, LNCS 3226, vol. World, Semantics for the Grid Databases, LNCS 3226, vol. 276-293, Lecture Notes in Computer Science, M. Bouzeghoub, 276-293, Lecture Notes in Computer Science, M. Bouzeghoub, C. Goble, V. Kashyap, and S. Spaccapietra, Eds., LNCS 3226 C. Goble, V. Kashyap, and S. Spaccapietra, Eds., LNCS 3226 Paris, France: Springer, 2004, pp. 278-295.Paris, France: Springer, 2004, pp. 278-295.

L. Kerschberg, H. Jeong, and W. Kim, “Emergent Semantics L. Kerschberg, H. Jeong, and W. Kim, “Emergent Semantics in Knowledge Sifter: An Evolutionary Search Agent based on in Knowledge Sifter: An Evolutionary Search Agent based on Semantic Web Services,” Semantic Web Services,” Journal of Data SemanticsJournal of Data Semantics, , Springer, 2006.Springer, 2006.

L. Kerschberg and H. Jeong, “Just-in-Time Knowledge L. Kerschberg and H. Jeong, “Just-in-Time Knowledge Management,” Keynote Talk, Third Conference on Management,” Keynote Talk, Third Conference on Professional Knowledge Management, April 10-13, 2005, Professional Knowledge Management, April 10-13, 2005, Kaiserslautern, Germany.Kaiserslautern, Germany.

L. Kerschberg and H. Jeong, “Ubiquitous Data Management in L. Kerschberg and H. Jeong, “Ubiquitous Data Management in Knowledge Sifter via Data-DNA,” International Workshop on Knowledge Sifter via Data-DNA,” International Workshop on Ubiquitous Data Management (UDM2005), Tokyo, Japan, April Ubiquitous Data Management (UDM2005), Tokyo, Japan, April 4, 20054, 2005

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Doctoral DissertationsDoctoral Dissertations Dr. Mohamed N. Bennani, “Autonomic Computing Dr. Mohamed N. Bennani, “Autonomic Computing

through Analytic Performance Models”, May 2006. through Analytic Performance Models”, May 2006. His advisor was Dr. Menascé.His advisor was Dr. Menascé.

Dr. Monchai Sopitakmol, “Experimental Study of Dr. Monchai Sopitakmol, “Experimental Study of Performance Sensitivity of Configurable Performance Sensitivity of Configurable Parameters of Web-based Systems” November 2004. Parameters of Web-based Systems” November 2004. His advisor was Dr. Menascé.His advisor was Dr. Menascé.

Dr. Randy Howard, “A Knowledge-Based Framework Dr. Randy Howard, “A Knowledge-Based Framework for Dynamic Semantic Web Services within Virtual for Dynamic Semantic Web Services within Virtual Organizations” October 2004. His advisor was Dr. Organizations” October 2004. His advisor was Dr. Kerschberg.Kerschberg.

Dr. Riki Morikawa, “A Framework for an Dr. Riki Morikawa, “A Framework for an Analytical Knowledge Base that Combines XML Analytical Knowledge Base that Combines XML Topic Maps, Bayesian Networks, and the Concept Topic Maps, Bayesian Networks, and the Concept of Network Scenarios for Enhanced Knowledge of Network Scenarios for Enhanced Knowledge Sharing” July 2004. His advisor was Dr. Sharing” July 2004. His advisor was Dr. Kerschberg.Kerschberg.

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Knowledge Sifter Knowledge Sifter 22

Proof-of-Concept Proof-of-Concept DemonstrationDemonstration

http://knowledgesifter.gmhttp://knowledgesifter.gmu.edu/u.edu/

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KS Main Page for Query: KS Main Page for Query: RushmoreRushmore

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KS User Preference PaneKS User Preference Pane

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KS Google Map for “Rushmore, Mount” KS Google Map for “Rushmore, Mount” in SD, US.in SD, US.

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KS Google Earth for “Rushmore, KS Google Earth for “Rushmore,

Mount” in SD, US.Mount” in SD, US.

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KS Google Earth Page for “Rushmore Farm” KS Google Earth Page for “Rushmore Farm”

in Zambia.in Zambia.

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KS Image Results for “Rushmore, KS Image Results for “Rushmore, Mount” in SD, US.Mount” in SD, US.