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

1. Knowledge Sifter: Agent-Based Search over Heterogeneous Sources using Semantic Web Services Faculty: Dr. Larry Kerschberg and Dr. Daniel Menascé Students: Hanjo Jeong, Scott Mitchell and Ahmed Abu Jbara Affiliates: Drs. Riki Morikawa, Randy Howard, & Wooju Kim E-Center for E-Business, http://eceb.gmu.edu/ Volgenau School of Information Technology and Engineering George Mason University, Fairfax, Virginia Sponsored by the NGA: National Geospatial-Intelligence Agency

2. Presentation Outline • Goals of the Knowledge Sifter Project; • Knowledge Sifter Architecture for semantic querying, accessing, ranking and integrating information from heterogeneous data sources; • Specification and design of Knowledge Sifter Meta-Model for storing end-to-end scenario information in a Knowledge Repository; • Conclusions • Demonstration of Knowledge Sifter 2 Prototype.

3. Goals of Knowledge Sifter Project - 1 • To provide intelligence analysts with services to: • Specify queries related to their work tasks and • Retrieve, rank and integrate results from multiple sources; • To use the emerging Semantic Web to create an object-oriented view of people, places, things and events by aggregating and integrating information from multiple heterogeneous sources.

4. Goals of Knowledge Sifter Project - 2 • To use open standards to easily incorporate new ontologies and sources in a plug-and-play fashion: • Imagery Standards (Web Map Services and Web Feature Services, and ISO Standards 19115 and 19139); • Semantic Web (RDF, RDFS, Web Ontology Language – OWL) • Web Services and Semantic Web Services to allow sources to be easily discovered and incorporated into the Knowledge Sifter architecture. • To create an agent-based service-oriented architecture that takes user queries, enhances them semantically, submits them for processing against multiple heterogeneous sources, and ranks them according to the user’s preferences and the systems similarity metrics;

5. Goals of Knowledge Sifter Project - 3 • Monitor and capture the actions and artifacts of users, KS agents, and data sources so as to learn user patterns, system patterns and source patterns in order to evolve Knowledge Sifter over time. • To use data mining techniques on the knowledge repository to mine patterns such as: • User preferences, contexts, and biases; • System templates for Web services choreography; • Data source QoS, availability and authoritativeness. • Monitor sources in real-time for QoS and adjust query traffic using a Web services broker.

6. Knowledge Sifter Architecture • Three-layer architecture: User, Knowledge Management, and Data Sources, • Autonomous agents handle specialized tasks, • Multiple domain models, ontologies, and authoritative services; • Web services agent handles requests to multiple heterogeneous sources; • Ranking agent rates results based on user and system preferences.

7. Imagery Domain Model in UML • Imagery Domain Model is the image ontology; • An Image has several Features such as Date and Size, with their respective attributes. • An Image has a Source and contains Content such as a Person, Thing, or Place. • Classes are related by relationships and ISA relationships. • Classes have properties. • OWL schema of Imagery Domain Model used by Knowledge Sifter agents to instantiate a query and associated metadata. 7

8. User Layer • User Agent interacts with user to obtain information regarding query specification; • Cooperates with Preference Agent to provide personalized criteria for search preferences, authoritative sites, and result ranking evaluation rules; • Cooperates with Query Formulation Agent to convey user preferences and the user’s initial query.

9. Knowledge Management Layer • Query Formulation Agent consults the Ontology Agent to enhance the query with “semantic” concepts. • Ontology Agent uses Imagery Domain Model, authoritative name services, and associated ontologies to specify semantic search concepts and coordinates for objects of interest. • Authoritative Name Services include WordNet from Princeton University, GNIS from USGS, and GEONet from NGA. • Query Formulation Agent receives the semantic query and passes it to the Web Services Agent for processing.

10. Knowledge Management Layer • Web Services Agent • Decomposes the query into subqueries and determines which Web Services or wrapped sources should process the sub-queries; • Translates the subqueries into query format of local sources; • In the case of Web Services such as TerraServer, uses the SOAP message format specified by the WSDL; • Selects the appropriate data source with semantic quality, QoS, and availability factors; • Handles the choreography of web services execution; • Results, returned to Web Services Agent, are then sent to the Ranking Agent. • Ranking Agent • Ranks the resulting information according to user ranking preferences, source authoritativeness, similarity measures, etc. • Sends results to User Agent for presentation to the user.

11. Data Sources Layer • Web Services and wrappers used to link to data sources; • Heterogeneous data sources include, • Image metadata, image archives, XML-repositories, relational databases, the Web and the emerging Semantic Web. • Quality of Service Issues • Specification of performance and availability QoS goals. • QoS negotiation protocols. • Hierarchical caching to support scalability.

12. Knowledge Sifter Meta-Model • Meta-model describes agent interaction, KS artifacts, feedback by users, etc. • Meta-model serves as a schema for capturing and storing artifacts such as user queries, reformulated queries, data sources used, query results, ranked results, user feedback, etc.

13. Knowledge Sifter Meta-Model

14. Protégé Meta-Model Ontology 14

15. KS Meta-Model OWL Specification • OWL & RDF Specification generated automatically from Protégé specification. • Meta-model guides the functioning of Knowledge Sifter and captures the relevant data from the actual agent-based execution. • Data stored in MySQL database according to a relational database for meta-model.

16. Conclusions • The goals of Knowledge Sifter are to provide services for analysts to pose semantic queries to multiple heterogeneous sources without regard to the format or location of those resources. • KS is based on open standards – Imagery, Semantic Web and Web Services – allowing a plug-and-play semantic architecture. • KS uses authoritative name services to provide concept synonyms (WordNet), and object location services (GNIS and GNS). • KS sources are accessed via Web service API (TerraServer) or via wrappers. • Longer-term research will focus on: • Support for emergent semantics and evolution; • Collaborative filtering to inform users when others are interested in similar concepts; and • Mechanisms by which analysts may specify hypotheses or scenarios, and the evidence will be drawn from the multiple heterogeneous sources.

17. Knowledge Sifter Publicationshttp://eceb.gmu.edu/publications.html • L. Kerschberg, M. Chowdhury, A. Damiano, H. Jeong, S. Mitchell, J. Si, and S. Smith, “Knowledge Sifter: Agent-Based Ontology-Driven Search over Heterogeneous Databases using Semantic Web Services,” in Semantics for a Networked World, Semantics for the Grid Databases, LNCS 3226, vol. 276-293, Lecture Notes in Computer Science, M. Bouzeghoub, C. Goble, V. Kashyap, and S. Spaccapietra, Eds., LNCS 3226 Paris, France: Springer, 2004, pp. 278-295. • L. Kerschberg, H. Jeong, and W. Kim, “Emergent Semantics in Knowledge Sifter: An Evolutionary Search Agent based on Semantic Web Services,” Journal of Data Semantics, Springer, 2006. • L. Kerschberg and H. Jeong, “Just-in-Time Knowledge Management,” Keynote Talk, Third Conference on Professional Knowledge Management, April 10-13, 2005, Kaiserslautern, Germany. • L. Kerschberg and H. Jeong, “Ubiquitous Data Management in Knowledge Sifter via Data-DNA,” International Workshop on Ubiquitous Data Management (UDM2005), Tokyo, Japan, April 4, 2005 17

18. Doctoral Dissertations • Dr. Mohamed N. Bennani, “Autonomic Computing through Analytic Performance Models”, May 2006. His advisor was Dr. Menascé. • Dr. Monchai Sopitakmol, “Experimental Study of Performance Sensitivity of Configurable Parameters of Web-based Systems” November 2004. His advisor was Dr. Menascé. • Dr. Randy Howard, “A Knowledge-Based Framework for Dynamic Semantic Web Services within Virtual Organizations” October 2004. His advisor was Dr. Kerschberg. • Dr. Riki Morikawa, “A Framework for an Analytical Knowledge Base that Combines XML Topic Maps, Bayesian Networks, and the Concept of Network Scenarios for Enhanced Knowledge Sharing” July 2004. His advisor was Dr. Kerschberg.

19. Knowledge Sifter 2Proof-of-Concept Demonstration http://knowledgesifter.gmu.edu/

20. KS Main Page for Query: Rushmore

21. KS User Preference Pane

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

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

24. KS Google Earth Page for “Rushmore Farm” in Zambia.

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