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DAML Tools for Intelligent Information DAML Tools for Intelligent Information Annotation, Sharing and RetrievalAnnotation, Sharing and Retrieval
UMBCMIT Sloan School
Johns Hopkins University Applied Physics Lab
Tim FininBenjamin GrosofJames Mayfield
DAML PI meeting, 30 November 2004
1/18 UMBC/JHU/MIT 11/19/2004
Intelligent Information Annotation, Sharing and Retrieval
Overall Program SummaryOWL helps enable agents in open, heterogeneous, dynamic environments share knowledge and cooperate on tasks while managing privacy, commitments, trust, and security. To do this, we must ensure that:
– OWL integrates with common agent frameworks and standards– OWL integrates with other KR paradigms for real world reasoning -- rule
based systems, Bayesian reasoning, etc.– OWL permits IR systems to index and retrieve documents with text,
multimedia and knowledge
Travel Agents
Auction Service Agent
CustomerAgent
Bulletin BoardAgent
Market Oversight Agent
Request
Direct Buy
Report Direct Buy Transactions
BidBid
CFP
Report Auction Transactions
Report Travel Package
Report Contract
Proposal
Web Service Agents
2/18 UMBC/JHU/MIT 11/19/2004
Intelligent Information Annotation, Sharing and Retrieval
Technical Problem and Approach
• Current agent systems are not OWL enabled– Develop ontologies, software & tools to use OWL in FIPA systems– Develop a declarative, OWL based policy language to constrain and
advise agent behavior– Demonstrate and evaluate use of OWL & Agents in realistic applications
• Integrate OWL with rule-based & uncertainty reasoning– Work with groups to develop specifications for RuleML, SWRL, etc– Build prototype translation systems and demonstrations– Annotate OWL content with Bayesian info and generate BNs– Demonstrate utility for ontology mapping
• Information retrieval systems must work with RDF content– Augment existing IR systems like Google with Swangle terms– Develop Swoogle as a native RDF search engine
3/18 UMBC/JHU/MIT 11/19/2004
Intelligent Information Annotation, Sharing and Retrieval
Technical Progress
• Agents and OWL– Developed standards, ontologies and software to use OWL in FIPA
systems and demonstrated them in trading agents and pervasive com-puting applications
– Developed Rei policy language and demonstrated for security & priv-acy in web services, collaboration tools and pervasive computing
– WWW2005 Policy Management for Web Workshop• OWL and reasoning
– Helped lead efforts to integrate rule reasoning into the semantic web (RuleML, SWRL, SWRL FOL) resulting in solid specs
– Developed tools to translate the above to standard rule engine form– Developed an ontology to annotate OWL content with Bayesian info
and translate the ensemble into a Bayesian Network• OWL and Information Retrieval
– Developed Swoogle, a crawler-based IR system for RDF documents– Developed Swangling, a technique to allow standard IR engines (e.g.,
Google) to index RDF triples in retrievable form
D
D
4/18 UMBC/JHU/MIT 11/19/2004
Intelligent Information Annotation, Sharing and Retrieval
Milestones and Accomplishments2004 2005
TAGA
SweetRules v2
Rei
F-OWL reasoner
Swoogle
Swangler
Bayes OWL
Joint Committee
W3C WebOnt Working group SWSI & SWSA
5+/7?/2?28/5/525/4/120/1/04/0/0
RGBs
s
ss
s
s
s
s
Distributed IDS
Mogatu
OO O
O
O
RuleML
O
O
DLP s
s
2001 2002 2003
Agent &Systems
ITTALKS
Vigil Soupa Cobra sO O
SweetJessKR
OWLIRIR
specs
Papers/MS/PhD
s
Legend
Softwareor service
Ontology
Ph.D. Dissertation
Spec impact
O
5
http://daml.org/IT TALKSIT TALKS
UMBC, JHU/APL, and MIT/Sloan are working together on a set of issues to be integrated into agent-based applications involving search and using rule-based reasoning: UMBC: Integrating communicating agents, DAML and the Web; JHU APL: DAML and information retrieval; and MIT Sloan School: DAML, rules based technology and distributed belief
Features•Generation from DB to DAML and HTML mediated by MySQL, Java servlets, and JSP.
•Generation of DAML descriptions and user profiles from HTML forms.
•Creation & use of DAML-encoded user modelsdescribing interests and ontology extensions.
•Ontologies for events, people, places, schedules,topics, etc.
•Automatic HTML form (pre) filling from DAML.•Syncing of talks with Palm calendars via Coola.•Automatic classification of talks into topicontology
•A XSB-based DAML/RDF reasoning engine.
•Agent-based services:• ITTALKS agent with KQML API using DAMLas content language
• Intelligent matching of people and talks based on interests, locations and schedules.
• Agents using both Jackal and FIPA’s Java Message Service
• Notification via email and mobile devices via SMS and WML.
• Discovery of relevant background papers from NEC CiteSeer
• Automatic generation and maintenance of user models
• Talk recommendations via collaborative filtering• Integration with STP (Smart Things and Places) ubiquitous computing project
Ittalks.org a database driven web site of IT related talks at UMBC and other institutions. The database contains information on
– Seminar events– People (speakers, hosts, users, …)– Places (rooms, institutions, …)
This database is used to dynamically generate web pages and DAML descriptions for the talks and related information and serves as a focal point for agent-based services relating to these talks. To add your organization to ittalks.org and receive a domain (e.g., mit.ittalks.org) contact info@ittalks.org. See http://daml.org/ for more information on DAML and the semantic web.
http://ittalks.org/
UMBCAN HONORS UNIVERSITY IN MARYLAND
Webserver + Javaservlets
DAMLreasoning
engine
DAMLreasoning
engine
<daml></daml><daml>
</daml><daml></daml><daml>
</daml>
DAML files
Agents
Databases
People
RDBMSRDBMSDB
Email, HTML, SMS, WAP
FIPA ACL, KQML, DAML
SQLHTTP, KQML, DAML, Prolog
MapBlast, CiteSeer, Google, …HTTP
HTTP, WebScraping
WebServices
Acknowledgements: Funded by DARPA, contract F30602-00-2-0591. Students: H. Chen, F. Perich, L. Kagal, S. Tolia, Y. Zou, J. Sachs, S. Kumar and M. Gandhe. Faculty: T. Finin, A. Joshi, Y. Peng, R. Cost, C. Nicholas, R. Masuoka
6/18 UMBC/JHU/MIT 11/19/2004
Rei Policy Spec Language
• Rei is a product of Lalana Kagal’s dissertation (2004)• An OWL based declarative policy language• Models deontic concepts of permissions,
prohibitions, obligations and dispensations• Meta policies resolve conflicts• Speech acts can dynamically modify policies• Used to model different kinds of policies
Security; privacy; team formation/collaboration/maintenance; conversation constraints
• Use at UMBC, in joint DAML projects andby Global Infotek and Fujitsu
7
TAGA: Travel Agent Game in Agentcities
http://taga.umbc.edu/
TechnologiesFIPA (JADE, April Agent Platform)
Semantic Web (RDF, OWL)
Web (SOAP,WSDL,DAML-S)
Internet (Java Web Start )
FeaturesOpen Market Framework
Auction Services
OWL message content
OWL Ontologies
Global Agent Community
MotivationMarket dynamicsAuction theory (TAC)Semantic webAgent collaboration (FIPA & Agentcities)
Travel Agents
Auction Service Agent
CustomerAgent
Bulletin BoardAgent
Market Oversight Agent
Request
Direct Buy
Report Direct Buy Transactions
BidBid
CFP
Report Auction Transactions
Report Travel Package
Report Contract
Proposal
Web Service Agents
Ontologieshttp://taga.umbc.edu/ontologies/
travel.owl – travel concepts
fipaowl.owl – FIPA content lang.
auction.owl – auction services
tagaql.owl – query language
FIPA platform infrastructure services, including directory facilitators enhanced to use OWL-S for service discovery
Owl for representation and reasoning
Owl for service
descriptions
Owl for negotiation
Owl as a content language
Owl for publishing
communicative acts
Owl for contract
enforcement
Owl for modeling
trust
Owl for authorization
policies
Owl for protocol
description
OWL Everywhere
Context Broker Architecturehttp://cobra.umbc.edu
CoBrA FeaturesCoBrA Features[1] Uses OWL for context modeling and reasoning
[2] Uses abductive reasoning to detect and resolve inconsis-tent context knowledge
[3] Extend the REI policy language for privacy protection
[4] Adopt the FIPA standards for communication & knowledge sharing
EasyMeeting an intelligent meeting room prototype that provides services forspeakers, audience & organizers based on their situational needs.
9/18 UMBC/JHU/MIT 11/19/2004
RGB: Research Group in a Boxeating our own dog food
• The UMBC ebiquity portal exposes ts con-tent in RDF using a set of OWL ontologies for papers, people, projects, photos, etc.
• Lab members can add arbitrary RDF assertions about any of the portal’s objects.
• It’s designed as a modular, configurable package using O/S software that others can use to create their own portals.
• An reasoning component is planned to apply ontology sanctioned inferences & heuristics.
People
Papers
http://ebiquity.umbc.edu/
Photos
10/18 UMBC/JHU/MIT 11/19/2004
Intelligent Information Annotation, Sharing and Retrieval
Adding Rules to OWL
• Theory, algorithms and concepts– Combining LP rules + ontologies
(1) Predicate refers to ontology URI. (2) Description Logic Programs KR and fusion. (3) Courteous inheritance for OO default inheritance. (4) In-Progress: hypermonotonic reasoning to unify nonmonotonic LP with FOL.
– Translation between rule (+ontology) systems/KRs• SweetJess: Situated Courteous LP ↔ production rules
– In-Progress: Combine {Courteous LP, RuleML} with F-Logic Programs (Hilog, frame syntax) for SWSL-Rules
• Tools: SweetRules, V1, V2– SweetJess, SweetOnto (DLP), SweetPH (Process Handbook),
SweetXSB, SweetCR (CommonRules), core architecture• Specifications
– RuleML (co-led), SWRL (co-led), FOL SWRL, FOL RuleML. SWSL Rules and FOL (co-led)
demodemodemo
11/18 UMBC/JHU/MIT 11/19/2004
Intelligent Information Annotation, Sharing and Retrieval
Adding Rules to OWL
• Services and security application scenarios– Contracts: SweetDeal prototype– Trust policies– SWSL requirements analysis including match of nonmonotonic LP to
majority of SWS tasks• Metrics
– KR expressiveness and coverage of translation/merging/ inferencing– Importance and number of distinct rule/ontology systems that
interoperate– Scalability of inferencing for SCLP
• See: Rules plenary session for details • See: Demonstrations of SweetRules and SweetDeal
12
Unified Ontology Support using Bayesian Networks
MotivationMotivationReasoning within an ontology is un-certain due to noisy/incomplete dataDL based reasoning is over-generalized and inadequateRelations between concepts in different ontologies are inherently uncertain
ApproachApproachTranslating OWL ontologies to Bayesian networks (BNs)Concept mapping as conditional probabilitiesJoining translated BNs (by probabilistic mappings) dynamicallyOntology reasoning (in and across ontologies) as Bayesian inference
Translation ProcessTranslation ProcessExtend OWL for probability annotationDefine structural translation rules from RDF graphs to directed acyclic graphs of Bayesian networksConstruct conditional probability tables for individual variables in the BN DAG.
BN1
onto1
P-onto1Probabilistic ontological information
onto2
P-onto2
BN2
Probabilistic annotation
OWL-BN translation
concept mapping
Probabilistic ontological information
Student: Z. Ding. Faculty: Dr. Y. Peng, Dr. T. Finin, Dr. A. Joshi. Ack: DARPA Contract F30602-00-2-0591. 09/03.
13
Unified Ontology Support using Bayesian Networks
MotivationMotivationReasoning within an ontology is un-certain due to noisy/incomplete dataDL based reasoning is over-generalized and inadequateRelations between concepts in different ontologies are inherently uncertain
ApproachApproachTranslating OWL ontologies to Bayesian networks (BNs)Concept mapping as conditional probabilitiesJoining translated BNs (by probabilistic mappings) dynamicallyOntology reasoning (in and across ontologies) as Bayesian inference
Translation ProcessTranslation ProcessExtend OWL for probability annotationDefine structural translation rules from RDF graphs to directed acyclic graphs of Bayesian networksConstruct conditional probability tables for individual variables in the BN DAG.
Derived Conditional Probability TablesStudent: Z. Ding. Faculty: Dr. Y. Peng, Dr. T. Finin, Dr. A. Joshi. Ack: DARPA Contract F30602-00-2-0591. 09/03.
14
SWOs
SWIs
HTMLdocuments
Images
CGI scripts
Audiofiles
Videofiles
SWD = SWO + SWI
service
SwoogleSearch
SwoogleStatistics
demodemodemoSwoogle Search
Ontology Dictionary
analysis
SWOOGLE 2
The web, like Gaul, is divided into three parts: the regular web (e.g. HTML), Semantic Web Ontologies (SWOs), and Semantic Web Instance files (SWIs)
Human usersWeb ServerOntologyDictionary
Web Service Intelligent Agents
IR analyzer SWD analyzer
SWD Cache SWD Metadata
SWD Readerdigest
Contributors include Tim Finin, Anupam Joshi, Yun Peng, R. Scott Cost, Jim Mayfield, Joel Sachs, Pavan Reddivari, Vishal Doshi, Rong Pan, Li Ding, and Drew Ogle. Partial research support was provided by DARPA contract F30602-00-0591 and by NSF by awards NSF-ITR-IIS-0326460 and NSF-ITR-IDM-0219649. November 2004.
http://swoogle.umbc.edu/Swoogle uses four kinds of crawlers to discover semantic web documents and several analysis agents to compute metadata and relations among documents and ontologies. Metadata is stored in a relational DBMS. Services are provided to people and agents.
A SWD’s rank is a function of its type (SWO/SWI) and the rank and types of the documents to which it’s related.
SWD IR Engine
SWD RankSwoogle Statistics
Swoogle puts documents into a character n-gram based IR engine to compute document similarity and do retrieval from queries
The WebThe Web
CandidateURLs Web Crawler
discovery
Swoogle provides services to people via a web interface and to agents as web services.
6,800,000Individuals4,200Ontologies
53,000Properties45,000,000Triples
95,000Classes310,000SWDs
Statistics as of November 2004
15
OWL Search usingOWL
document
OWLDocument +
Swangledtriples
SwangleSearchQuery Triple
OWLDocument +
Swangledtriples
<j.0:SwangledTriple><rdfs:comment>Swangled text for
[<http://www.xfront.com/owl/ontologies/camera/#Digital>,<http://www.w3.org/2000/01/rdf-schema#subClassOf>, <http://www.xfront.com/owl/ontologies/camera/#PurchaseableItem>
]</rdfs:comment><j.0:swangledText>BE52HVKU5GD5DHRA7JYEKRBFVQ</j.0:swangledText><j.0:swangledText>WS4KYRWMO3OR3A6TUAR7IIIDWA</j.0:swangledText> <j.0:swangledText>2THFC7GHXLRMISEOZV4VEM7XEQ</j.0:swangledText><j.0:swangledText>HO2H3FOPAEM53AQIZ6YVPFQ2XI</j.0:swangledText><j.0:swangledText>6P3WFGOWYL2DJZFTSY4NYUTI7I</j.0:swangledText> <j.0:swangledText>N656WNTZ36KQ5PX6RFUGVKQ63A</j.0:swangledText> <j.0:swangledText>IIVQRXOAYRH6GGRZDFXKEEB4PY</j.0:swangledText>
</j.0:SwangledTriple>
WebSearchEngine
Filters SemanticMarkup
InferenceEngine
LocalKB
SemanticMarkup
SemanticMarkup
Extractor
Encoder
RankedPages
SemanticWeb Query
EncodedMarkup
TextQuery
TextFiltersText
[<http://www.xfront.com/owl/ontologies/camera/#Digital>,<http://www.w3.org/2000/01/rdf-schema#subClassOf>, <http://www.xfront.com/owl/ontologies/camera/#PurchaseableItem>
]
Inference
[<http://www.xfront.com/owl/ontologies/camera/#Digital>,<http://www.w3.org/2000/01/rdf-schema#subClassOf>, <http://www.xfront.com/owl/ontologies/camera/#PurchaseableItem>
][<http://www.xfront.com/owl/ontologies/camera/#Digital>,<http://www.w3.org/2002/07/owl#Thing>, <http://www.xfront.com/owl/ontologies/camera/#PurchaseableItem>
][<http://www.xfront.com/owl/ontologies/camera/#Digital> ,< http://www.w3.org/2002/07/owl#Thing >, < http://www.w3.org/2002/07/owl#Thing >
]
Swangler
Vision
Indexing and Search
Swangler
SwangleToolOWL Triples
JENA
16/18 UMBC/JHU/MIT 11/19/2004
Intelligent Information Annotation, Sharing and Retrieval
Transition/Handoff• Specifications
– Participated in W3C WebOnt, SWSA, SWSI– Co-founded RuleML, Co-led SWRL
• Software products– Swangler, F-OWL, SweetRules (+ SweetJess), Rei, Swoogle
• Major demonstration systems– ITTALKS, TAGA, EasyMeeting, Groove Team Agent, ebiquity site,
SweetDeal• Services
– Swoogle• Papers, tutorials, workshops
– 82 refereed papers, 17 MS theses, 8 PhD dissertations, 6 conference tutorials, several workshops
• Users– Swoogle has 100s of users, Rei used by ~4 other groups, SweetJess
and F-OWL each used by several groups
17/18 UMBC/JHU/MIT 11/19/2004
Intelligent Information Annotation, Sharing and Retrieval
Remaining Issues
SweetRules authoring tools and studio/IDE environment; continued maintenance beyond 2005
SweetRules adoption
Extend OWL2BN translation algorithm to properties. Can prob-abilities be estimated from Swoogle data? Do mapping usecase.
Bayes OWL completion
Develop tools to enable Swangler and Swoogle to work with documents that embed RDF in XHTML.
IR over mixed RDF and text
Build a query interface for swangled Semantic Web documents. Develop a good usecase (intranet?)
Swangler lacks query I/F
Extend Swoogle’s database to include all instance data. How well will it scale?
Swoogle needs instance data
Incorporate SWRL/RuleML languages. Can we compile Rei policies to OWL+SWRL?
Rei policy langu-age needs rules
Complete design and implementation and documentation of existing components (e.g., SweetJess) and SWRL support
SweetRules polishing
RemediationIssue
18/18 UMBC/JHU/MIT 11/19/2004
Intelligent Information Annotation, Sharing and Retrieval
SummaryWe’ve demonstrated that• OWL helps agents in open, dynamic environments to share
knowledge and manage privacy, commitments, trust, and security.– Demonstration: ITTALKS system, TAGA multiagent system, SOUPA
ontology for pervasive computing• OWL can be integrated with other KR paradigms for real
world reasoning– Demonstration: SweetJess, SweetRules, Bayes OWL, Rei
• OWL is compatible with the information retrieval paradigm– Demonstration: Swoogle as an RDF search engine, Swangling
permits IR systems to index and retrieve documents with text, multimedia, and knowledge.
• OWL facilitates the sharing of knowledge thru web portals– Demonstration: ebiquity web pages
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