Semantic Information Systems

1
Semantic Information Systems

• Tim Finin, Anupam Joshi,Charles Nicholas and Tim
  Oates
• 17 December 2004

2
Agenda

• 1000 - 1010 Arrival, agenda (Nicholas)
• 1010 - 1020 Introduction (Finin)
• 1020 - 1030 Semantic web and IR (Finin)
• 1030 - 1040 Trust, provenance policy (Joshi)
• 1040 - 1050 Relational learning on the semantic
  web (Oates)
• 1050 - 1200 Discussion (Nicholas)

3
Semantic Information Systems

• A new generation of information systems that
  include semantics in a more fundamental way,
  e.g.,
• Computer understandable metadata
• Standards for expressing knowledge as well as
  data
• Machine learning for knowledge discovery and
  adaptation
• Automated service discovery, composition
  invocation
• Cooperating agent based systems
• Why now?
• Metcalfes Law and the web
• Semantic web languages
• Maturing learning technologies
• Is this the endgame?
• Hardly, its the opening up of a new approach
  toward the familiar long term goal of building
  intelligent systems

4
IntroductionSemantic Web
5

• XML is Lisp's bastard nephew, with uglier syntax
  and no semantics. Yet XML is poised to enable the
  creation of a Web of data that dwarfs anything
  since the Library at Alexandria.
• -- Philip Wadler, Et tu XML? The fall of
  the relational empire, VLDB, Rome, September
  2001.

6

• The web has made people smarter. We need to
  understand how to use it to make machines
  smarter, too.
• -- Michael I. Jordan, paraphrased from a
  talk at AAAI, July 2002 by Michael Jordan
  (UC Berkeley)

7

• The Semantic Web will globalize KR, just as the
  WWW globalize hypertext
• -- Tim Berners-Lee

8

• The multi-agent systems paradigm and the web
  both emerged around 1990. One has succeeded
  beyond imagination and the other has not yet made
  it out of the lab.
• -- Anonymous, 2001

9
(No Transcript)
10
Origins of the Semantic Web

• TBLs vision for the web (89)
• Guhas MCF (94)
• XMLMCFgtRDF (96)
• RDFOOgtRDFS (99)
• RDFSKRgtDAMLOIL (00)
• W3Cs SW activity (01)
• W3Cs OWL (03)
• 2M SWDs on web (04)

TBL

• http//www.w3.org/History/1989/proposal.html

11
TBLs semantic web vision
The Semantic Web will globalize KR, just as the
WWW globalize hypertext -- Tim Berners-Lee
we arehere
12
RDF is the first SW language
Graph
XML Encoding
ltrdfRDF ..gt lt.gt lt.gt lt/rdfRDFgt
RDF Data Model
Good For HumanViewing
Good for MachineProcessing
Triples
stmt(docInst, rdf_type, Document) stmt(personInst,
rdf_type, Person) stmt(inroomInst, rdf_type,
InRoom) stmt(personInst, holding,
docInst) stmt(inroomInst, person, personInst)
RDF is a simple language for building graph based
representations
Good For Reasoning
13
Simple RDF Example
http//umbc.edu/finin/talks/idm02/
dcTitle
Intelligent Information Systemson the Web and
in the Aether
dcCreator
bibAff
bibemail
http//umbc.edu/
bibname
finin_at_umbc.edu
Tim Finin
14
XML encoding for RDF
ltrdfRDF xmlnsrdf"http//www.w3.org/1999/02/22-r
df-syntax-ns" xmlnsdc"http//purl.org/dc/el
ements/1.1/" xmlnsbib"http//daml.umbc.edu/o
ntologies/bib/"gt ltdescription about"http//umbc.e
du/finin/talks/idm02/"gt ltdctitlegtIntelligent
Information Systems on the Web and in the
Aetherlt/dcTitlegt ltdccreatorgt
ltdescriptiongt ltbibNamegtTim
Fininlt/bibNamegt ltbibEmailgtfinin_at_umbc.edult/
bibEmailgt ltbibAff resource"http//umbc.ed
u/" /gt lt/descriptiongt lt/dcCreatorgt lt/descr
iptiongt lt/rdfRDFgt
15
RDF Schema (RDFS)

• RDF Schema adds taxonomies forclasses
  properties
• subClass and subProperty
• and some metadata.
• domain and rangeconstraints on properties
• Several widely usedKB tools can importand
  export in RDFS

• Stanford Protégé KB editor
• Java, open sourced
• extensible, lots of plug-ins
• provides reasoning server capabilities

16
From RDF to OWL

• An OWL ontology is a set of RDF statements
• OWL defines semantics for certain statements
• Does NOT restrict what can be said -- documents
  can include arbitrary RDF
• But no OWL semantics for non-OWL statements
• Adds capabilities common to description logics
• cardinality constraints, defined classes (gt
  classification), equivalence, local restrictions,
  disjoint classes, etc.
• More support for ontologies
• Ontology imports ontology, versioning,
• But not (yet) variables, quantification, rules
• A complete OWL reasoning is significantly more
  complex than a complete RDFS reasoner.

17
OWL in One Slide

• ltrdfRDF xmlnsrdf "http//w3.org/22-rdf-syntax-n
  s"
• xmlnsrdfshttp//w3.org/rdf-schemagt
  xmlnsowl"http//www.w3.org/2002/07/owlgt
• ltowlOntology rdfabout""gt
• ltowlimports rdfresource"http//owl.org/owl
  oil"/gt
• lt/owlOntologygt
• ltowlClass rdfID"Person"gt
• ltrdfssubClassOf rdfresource"Animal"/gt
• ltrdfssubClassOfgt
• ltowlRestrictiongt
• ltowlonProperty rdfresource"hasParent"/gt
• ltowltoClass rdfresource"Person"/gt
• lt/owlRestrictiongt
• lt/rdfssubClassOfgt
• ltrdfssubClassOfgt
• ltowlRestriction owlcardinality"1"gt
• ltowlonProperty rdfresource"hasFather"/gt
• lt/owlRestrictiongt
• lt/rdfssubClassOfgt
• lt/owlClassgt

OWL is built on top of XML and RDF
It allows the definition, sharing, composition
and use of ontologies
OWL is a frame based knowledge representation
language
It can be used to add metadata about anything
which has a URI.
URIs are a W3C standard generalizing URLs
everything has a URI
18
And on to Rules

• Approaches for adding knowledge in the form of
  rules have been developed and prototyped and are
  entering a standardization phase
• SWRL RuleML, logimplies
• Motivation
• Extending/augmenting OWLs expressivity
• Supporting policies, negotiation, justification,
  argumentation, etc.

19
A usecase FOAF

• FOAF (Friend of a Friend) is a simple ontology to
  describe people and their social networks.
• See the foaf project page http//www.foaf-project
  .org/
• We recently crawled the web and discovered over
  1,500,000 valid RDF FOAF files.
• Most of these are from blogging sites which use
  foaf to publish basic user info
• See http//apple.cs.umbc.edu/semdis/wob/foaf/

ltfoafPersongt ltfoafnamegtTim Fininlt/foafnamegt ltfo
afmbox_sha1sumgt241037262c252elt/foafmbox_sha1sum
gt ltfoafhomepage rdfresource"http//umbc.edu/fi
nin/" /gt ltfoafimg rdfresource"http//umbc.edu/
finin/images/passport.gif" /gt lt/foafPersongt
20
FOAF why RDF? Extensibility!

• FOAF vocabulary provides 50 basic terms for
  making simple claims about people
• FOAF files can use RDF terms from other
  ontologies too RSS, MusicBrainz, Dublin Core,
  Wordnet, Creative Commons, blood types,
  starsigns,
• RDF guarantees freedom of independent extension
• OWL provides fancier data-merging facilities 
• Result Freedom to say what you like, using any
  RDF markup you want, and have RDF crawlers merge
  your FOAF documents with others and know when
  youre talking about the same entities. 

21
No free lunch!

• Consequence
• We must plan for lies, mischief, mistakes, stale
  data, slander
• Dataset is out of control, distributed, dynamic
• Importance of knowing who-said-what
• Anyone can describe anyone
• We must record data provenance
• Modeling and reasoning about trust is critical
• Legal, privacy and etiquette issues emerge
• Welcome to the real world

22
From where will the markup come?

• A few authors will add it manually.
• More will use annotation tools.
• SMORE Semantic Markup, Ontology and RDF Editor
• Intelligent processors (e.g., NLP) can understand
  documents and add markup (hard)
• Machine learning powered information extraction
  tools show promise
• Lots of web content comes from databases we can
  generate SW markup along with the HTML
• See http//ebiquity.umbc.edu/

23
From where will the markup come?

• In many tools, part of the metadata information
  is present, but thrown away at output
• e.g., a business chart can be generated by a
  tool
• it knows the structure, the classification,
  etc. of the chart
• but, usually, this information is lost
• storing it in metadata is easy!
• So semantic web aware tools can produce lots of
  metadata
• E.g., Adobes use of its XMP platform

24
Use Cases

• Adding machine understandable metadata to images,
  documents, software, services, etc
• In support of discovering, indexing, and
  retrieving
• Knowledge sharing and semantic interoperability
• Exchanging data and knowledge (rules,
  definitions) for negotiation, argumentation,
  explanations, policies
• Semantic web/grid/agent services
• Annotating service descriptions with semantic
  info for discovery, composition, invocation,
  monitoring, etc.
• Relational learning on the web
• Boosted with KR reasoning and constraints

25
Information Retrieval and the Semantic Web
26
Why use IR techniques?

• We will want to retrieve over the structured and
  unstructured parts of a Semantic Wed Document
  (SWD)
• We should prepare for the appearance of text
  documents with embedded SW markup
• We may want to get our SWDs into conventional
  search engines, such as Google.
• IR techniques also have some unique
  characteristics that may be very useful
• e.g., ranking matches, measuring
  similaritybetween documents, relevance
  feedback,etc.

27
What we have done

• Developed Swoogle a crawler based retrieval
  system for SWDs
• Developed and implemented a technique to get
  Google to index and retrieve SWDs
• Prototyped (twice) an ngram based IR engine for
  SWDs
• Used these in several demonstration systems

28
Swoogle Search
demo
SWD SWO SWI
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)
Web Server
Human users
Ontology Dictionary
OntologyDictionary
SwoogleStatistics
SwoogleSearch
Web Service
Intelligent Agents
service
IR analyzer
SWD analyzer
analysis
SWD Metadata
SWD Cache
digest
SWD Reader
The Web
Candidate URLs
SWD Rank
Web Crawler
Swoogle Statistics
discovery
A SWDs rank is a function of its type (SWO/SWI)
and the rank and types of the documents to which
its related.
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.
http//swoogle.umbc.edu/
Statistics as of November 2004
SWD IR Engine
Swoogle provides services to people via a web
interface and to agents as web services.
Swoogle puts documents into a character n-gram
based IR engine to compute document similarity
and do retrieval from queries
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.
29
Swoogle IR Search

• This is work in progress, not yet fully
  integrated into Swoogle
• Documents are put into an ngram IR engine (after
  processing by Jena) in canonical XML form
• Each contiguous sequence of N characters is used
  as an index term (e.g., N5)
• Queries processed the same way
• Character ngrams work almost as well as words but
  have some advantages
• No tokenization, so works well with artificial
  languages and agglutinative languages
• gt good for RDF!

30
Why character n-grams?

• Suppose we want to find ontologies for time
• We might use the following query
• time temporal interval point before after during
  day month year eventually calendar clock duration
  end begin zone
• And have matches for documents with URIs like
• http//foo.com/timeont.owltimeInterval
• http//foo.com/timeont.owlCalendarClockInterval
• http//purl.org/upper/temporal/t13.owltimeThing

31
Another approach URIs as words

• Remember ontologies define vocabularies
• In OWL, URIs of classes and properties are the
  words
• So, take a SWD, reduce to triples, extract the
  URIs (with duplicates), discard URIs for blank
  nodes, hash each URI to a token (use MD5Hash),
  and index the document.
• Process queries in the same way
• Variation include literal data (e.g., strings)
  too.

32
Harnessing Google

• Google started indexing RDF documents some time
  in late 2003
• Can we take advantage of this?
• Weve developed techniques to get some structured
  data to be indexed by Google
• And then later retrieved
• Technique give Google enhanced documents with
  additional annotations containing Swangle Terms

33
Swangle definition

• swangle
• Pronunciation swang-glFunction transitive
  verbInflected Forms swangled swangling
  /-g(-)ling/Etymology Postmodern English,
  from C mangle, Date 20th century
• 1 to convert an RDF triple into one or more IR
  indexing terms
• 2 to process a document or query so that its
  content bearing markup will be indexed by an
  IR system
• Synonym see tblify
• - swangler /-g(-)lr/ noun

34
Swangling

• Swangling turns a SW triple into 7 word like
  terms
• One for each non-empty subset of the three
  components with the missing elements replaced by
  the special dont care URI
• Terms generated by a hashing function (e.g., MD5)
• Swangling an RDF document means adding in triples
  with swangle terms.
• This can be indexed and retrieved via
  conventional search engines like Google
• Allows one to search for a SWD with a triple that
  claims Ossama bin Laden is located at X

35
A Swangled Triple

• ltrdfRDF
• xmlnss"http//swoogle.umbc.edu/ontologies/swan
  gle.owl"
• lt/rdfgt
• ltsSwangledTriplegt ltsswangledTextgtN656WNTZ36KQ5
  PX6RFUGVKQ63Alt/sswangledTextgt
  ltrdfscommentgtSwangled text for
  http//www.xfront.com/owl/ontologies/camera/Came
  ra, http//www.w3.org/2000/01/rdf-schema
  subClassOf, http//www.xfront.com/owl/ontol
  ogies/camera/PurchaseableItem
  lt/rdfscommentgt ltsswangledTextgtM6IMWPWIH4YQI4IM
  GZYBGPYKEIlt/sswangledTextgt ltsswangledTextgtHO2H
  3FOPAEM53AQIZ6YVPFQ2XIlt/sswangledTextgt
  ltsswangledTextgt2AQEUJOYPMXWKHZTENIJS6PQ6Mlt/sswan
  gledTextgt ltsswangledTextgtIIVQRXOAYRH6GGRZDFXKEE
  B4PYlt/sswangledTextgt ltsswangledTextgt75Q5Z3BYAK
  RPLZDLFNS5KKMTOYlt/sswangledTextgt
  ltsswangledTextgt2FQ2YI7SNJ7OMXOXIDEEE2WOZUlt/sswan
  gledTextgtlt/sSwangledTriplegt

36
Whats the point?

• Wed like to get our documents into Google
• The Swangle terms look like words to Google and
  other search engines.
• We use cloaking to avoid having to modify the
  document
• Add rules to the web server so that, when a
  search spider asks for document X the document
  swangled(X) is returned
• Caching makes this efficient

37
(No Transcript)
38
Trust and Provenance
39
Actionable Information Trust and Provenance
Bob
Modeling Alices trustworthiness
by default
?
Bob believes M
40
How trust and provenance work
http//foo.com/alice.rdf
fooGeorge exlivesIn exEurope
exgeorge spacelivesIn spaceUS
believes
distrusts
5
source
1
fooGeorge exlivesIn exUS
Eve
believes
4
Where does George live ?
believes
2
trusts
3
Alice
Bob
http//foo.com/bob.rdf
http//foo.com/alice.rdf dccreator exalice
exbob wobtrusts exalice
exbob wobdistrusts exEve
41
An Example
HUMINT
Terrorist Groups
Osama Bin Laden
Osama Bin Laden
listedIn
memberOf
relatedTo
locatedIn
Al-Qaeda
Afghanistan
Mr. Y
ownedBy
Department of State
Organization B
FOO News
SIGINT
Afghanistan
Organization B
locatedIn
invests
basedIn
Kabul
Company A
Kabul
CIA World Fact Book
locatedIn
NASDAQ
isPresidentOf
Mr.X
Company A
US
42
How do provenance and trust help?

• Disambiguation
• Provenance helps locating matching or relevant
  resources
• Data access
• Knowledge can be grouped by Provenance besides
  Topic
• (Trust Provenance) enables inference on only
  credible information sources, and thus controls
  space complexity
• Credibility analysis
• (Trust) models imperfect information
• (Provenance) enables explicit trace of
  justification
• (Trust Provenance) enable social justification
  as alternative of logical justification (truth
  maintenance system)
• Conclusive inference by adopting trusted beliefs
• Resolve inconsistency by consensus
• (Trust Provenance) enables social warfare and
  thus social control on the SW

43
Trust Provenance Complementing Security
44
Issues

• Represent provenance trust
• Web Of Belief Ontology
• Create a computationally tractable model of trust
• How to (mathematically) model trust
• How to initialize and propagate trust
• Maintain Provenance
• Decide when apparently distinct sources are
  really based on the same underlying data
• Manage provenance trust information
• Extracted from the Semantic Web, online social
  networks, and online reputation systems
• Effective metadata based data access service

45
Controlling Information Gathering Behavior using
Policies

• Policies are rules of correct behavior
• Policies are normative and describe what should
  be done in an ideal world.
• Policies permit high-level control of self
  organizing, self managing, self healing entities
  
• Entities? These can be programs, services,
  agents, devices and people
• Using policies reduces the need to modify code in
  order to change systems behavior
• We assume modifying policies will be easier than
  modifying Java.
• Using policies allows humans to understand
  behavior without (always) looking at the code.

46
Behavior Specifications

• You MUST NOT use any information from nations in
  the Axis of Evil, unless expressly authorized by
  the DCI.
• You MUST check at least three variants of a
  hypothesis, including its negative
• You are OBLIGED to trust anyone that SecDef
  declares as trustworthy
• You SHOULD trust Safire about privacy but not
  about the middle east
• You are OBLIGED to materialize a new view
  whenever enough queries do joins across tables.
• You CAN NOT use the camera functionality of your
  handheld device in this building

47
Rei Policy Language

• Developed several versions of Rei, a policy
  specification language, encoded in (1) Prolog,
  (2) RDFS, (3) OWL
• Used to model different kinds of policies
• Authorization for services
• Privacy in pervasive computing and the web
• Conversations between agents
• Team formation, collaboration maintenance
• The OWL grounding enables policies that reason
  over SW descriptions of actions, agents, targets
  and context (Domain knowledge)

48
Rei Policy Language

• Rei is a declarative policy language for
  describing policies over actions
• Reasons over domain dependent information
• Currently represented in OWL logical variables
• Based on deontic concepts
• Permission, Prohibition, Obligation, Dispensation
• Models speech acts
• Delegation, Revocation, Request, Cancel
• Meta policies
• Priority, modality preference

49
Hypothesis Generation

• Question answering will become hypothesis
  proving with the semantic web (Mayfields idea)
• How about hypothesis generation
• Can be very useful in avoiding group think
• Can we semi automatically generate hypotheses
• Variants of an existing hypothesis using domain
  knowledge
• When testing for X belongs to Al-Qaeda, test for
  X belongs to Jamat-ul-Dawa.

50
Relational Learning on the Semantic Web
51
Conclusions
52
Semantic Information Systems

• A new generation of information systems that
  include semantics in a more fundamental way
• Driven by a convergence of web technologies,
  machine learning, and mature KB techniques
• This is being done bottom up by pragmatists as
  well as by academic researchers
• Good use cases are plentiful
• Theres evidence that the technologies are being
  taken up

53
BackupSemantic Web
54
RDFS supports simple inferences
New and Improved! 100 Betterthan XML!!

• An RDF ontology plus some RDFstatements may
  imply additional RDFstatements.
• This is not true of XML.
• Example
• domain(parent,person)
• range(parent,person)
• subproperty(mother,parent)
• range(mother,woman)
• mother(eve,cain)
• This is part of the data model and not of the
  accessing/processing code

Implies subclass(woman,person)
parent(eve,cain) person(eve) person(cain)
woman(eve)
ontology
instance
55
Bayes OWL
Unified Ontology Support using Bayesian Networks

• Motivation
• Reasoning within an ontology is un-certain due to
  noisy/incomplete data
• DL based reasoning is over-generalized and
  inadequate
• Relations between concepts in different
  ontologies are inherently uncertain

• Translation Process
• Extend OWL for probability annotation
• Define structural translation rules from RDF
  graphs to directed acyclic graphs of Bayesian
  networks
• Construct conditional probability tables for
  individual variables in the BN DAG.

• Approach
• Translating OWL ontologies to Bayesian networks
  (BNs)
• Concept mapping as conditional probabilities
• Joining translated BNs (by probabilistic
  mappings) dynamically
• Ontology reasoning (in and across ontologies) as
  Bayesian inference

Student Z. Ding. Faculty Dr. Y. Peng, Dr. T.
Finin, Dr. A. Joshi. Ack DARPA Contract
F30602-00-2-0591. 09/03.
56
Bayes OWL
Unified Ontology Support using Bayesian Networks

• Motivation
• Reasoning within an ontology is un-certain due to
  noisy/incomplete data
• DL based reasoning is over-generalized and
  inadequate
• Relations between concepts in different
  ontologies are inherently uncertain

• Translation Process
• Extend OWL for probability annotation
• Define structural translation rules from RDF
  graphs to directed acyclic graphs of Bayesian
  networks
• Construct conditional probability tables for
  individual variables in the BN DAG.

• Approach
• Translating OWL ontologies to Bayesian networks
  (BNs)
• Concept mapping as conditional probabilities
• Joining translated BNs (by probabilistic
  mappings) dynamically
• Ontology reasoning (in and across ontologies) as
  Bayesian inference

Derived Conditional Probability Tables
Student Z. Ding. Faculty Dr. Y. Peng, Dr. T.
Finin, Dr. A. Joshi. Ack DARPA Contract
F30602-00-2-0591. 09/03.
57
IT TALKS
http//daml.org/
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 FIPAs 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

http//ittalks.org/

• 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_at_ittalks.org. See http//daml.org/ for more
  information on DAML and the semantic web.

UMBCAN HONORS UNIVERSITY IN MARYLAND
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
58
TAGA Travel Agent Game in Agentcities
Owl for protocol description
Owl for contract enforcement

• Technologies
• FIPA (JADE, April Agent Platform)
• Semantic Web (RDF, OWL)
• Web (SOAP,WSDL,DAML-S)
• Internet (Java Web Start )

• Features
• Open Market Framework
• Auction Services
• OWL message content
• OWL Ontologies
• Global Agent Community

• Motivation
• Market dynamics
• Auction theory (TAC)
• Semantic web
• Agent collaboration (FIPA Agentcities)

• Ontologieshttp//taga.umbc.edu/ontologies/
• travel.owl travel concepts
• fipaowl.owl FIPA content lang.
• auction.owl auction services
• tagaql.owl query language

Owl for representation and reasoning
Owl for publishing communicative acts
Owl for modeling trust
Owl for negotiation
OWL Everywhere
Owl as a content language
FIPA platform infrastructure services, including
directory facilitators enhanced to use OWL-S for
service discovery
Owl for service descriptions
Owl for authorization policies
http//taga.umbc.edu/
59
Context Broker Architecture
http//cobra.umbc.edu
CoBrA 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 for speakers, audience
organizers based on their situational needs.
60
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 portals objects.
• Its 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
Photos
http//ebiquity.umbc.edu/
61
BackupSW IR
62
Demo
Find Time Ontology (Swoogle Search)
1

• Digest Time Ontology
• Document view
• Term view

2
3
Find Term Person (Ontology Dictionary)

• Digest Term Person
• Class properties
• (Instance) properties

4
Swoogle Statistics
5
63
Find Time Ontology
Demo1
We can use a set of keywords to search ontology.
For example, time, before, after are basic
concepts for a Time ontology.
64
Usage of Terms in SWD
http//www.cs.umbc.edu/finin/foaf.rdf
http//foo.com/foaf.rdf
rdftype
rdftype
foafPerson
foafPerson

foafmbox
http//foo.com/foaf.rdffinin
finin_at_umbc.edu
finin_at_umbc.edu
foafmbox
http//xmlns.com/foaf/1.0/
populated Class
rdfssubClassOf
wordNetAgent
populated Property
foafPerson
rdftype
rdfsClass
rdfsdomain
defined Class
foafmbox
rdftype
defined Property
rdfProperty
defined Individual
65
Digest Time Ontology (term view)
Demo2(a)
TimeZone
before
.
intAfter
66
Document Metadata

• Web document metadata
• When/how discovered/fetched
• Suffix of URL
• Last modified time
• Document size
• SWD metadata
• Language features
• OWL species
• RDF encoding
• Statistical features
• Defined/used terms
• Declared/used namespaces
• Ontology Ratio
• Ontology Rank

• Ontology annotation
• Label
• Version
• Comment
• Related Relational Metadata
• Links to other SWDs
• Imported SWDs
• Referenced SWDs
• Extended SWDs
• Prior version
• Links to terms
• Classes/Properties defined/used

67
Digest Time Ontology (document view)
Demo2(b)
68
Find Term Person
Demo3
Not capitalized! URIref is case sensitive!
69
Term Metadata An integrated definition

• Class Definition
• rdfssubClassOf -- foafAgent
• rdfslabel Person

• Properties (from SWO)
• foafmbox
• foafname

• Properties (from SWI)
• foafname
• dctitle

foafPerson
70
Digest Term Person
Demo4
167 different properties
562 different properties
71
Demo5
Swoogle Statistics
72
Swoogle Architecture
data analysis
interface
IR analyzer
SWD analyzer
Web Server
Web Service
SWD Metadata
SWD Cache
metadata creation
Agent Service
SWD Reader
SWD discovery
The Web
Candidate URLs
Web Crawler
73
BackupTrust and Provenance
74
How trust and provenance work
http//foo.com/alice.rdf
fooGeorge exlivesIn exEurope
exgeorge spacelivesIn spaceUS
believes
distrusts
5
source
1
fooGeorge exlivesIn exUS
Eve
believes
4
Where does George live ?
believes
2
trusts
3
Alice
Bob
http//foo.com/bob.rdf
http//foo.com/alice.rdf dccreator exalice
exbob wobtrusts exalice
exbob wobdistrusts exEve
75
How trust and provenance work
http//foo.com/alice.rdf
fooGeorge exlivesIn exEurope
exgeorge spacelivesIn spaceUS
believes
distrusts
5
source
1
fooGeorge exlivesIn exUS
Eve
believes
4
Where does George live ?
believes
2
trusts
3
Alice
Bob
http//foo.com/bob.rdf
http//foo.com/alice.rdf dccreator exalice
exbob wobtrusts exalice
exbob wobdistrusts exEve
76
How provenance and trust help?

• Disambiguation
• Provenance helps locating matching or relevant
  resources
• Data access
• Knowledge can be grouped by Provenance besides
  Topic
• (Trust Provenance) enables inference on only
  credible information sources, and thus controls
  space complexity
• Credibility analysis
• (Trust) models imperfect information
• (Provenance) enables to explicit justification
  trace
• (Trust Provenance) enables social justification
  as alternative of logical justification (truth
  maintenance system)
• Conclusive inference by adopting trusted beliefs
• Resolve inconsistency by consensus
• (Trust Provenance) enables social warfare and
  thus social control on the SW

1. Introduction
77
Configuration

• Bizrate.com
• Epinion.com
• Normal, Zipf

• FOAF
• Epinion.com
• Scale free graph

• Referral policy
• Feedback policy
• Consult policy

• Spatial parameters

• Uniform
• Locality based

Belief Factory
Query Factory
Graph Factory
Location Factory
knows matrix
Belief matrix
Domain mapping
cost matrix
Query queue
Agent Society Creator
initialize
Inference
query

• Agent Society
• Trust Evolution
• Trust Propagation

input
Trust matrix
Analyzer
output
feedback
Evaluation
evaluate

• Statistics
• Accuracy
• Convergence time
• trust network structure

78
How could a Statement be justified

• I believe that
• Restaurants with good outlook are good
• Foo has good outlook

I believe that Good restaurants has good
outlook Foo has good outlook
deductive
abductive
Foo is a good restaurant
prima facie (at first view)
No better alternative
conclusive (mimic)
inductive
Ive been to Foo many times, and the food was
good!
My friends (who have similar taste as me ) said
so.
1. Introduction
79
Data Model
environment
Context
Restriction

• Local knowledge
• Belief and domain

input
derived

• Communication channel
• Communication cost

Trust Network Inference
evolves
Trust
output
Estimated Belief
approximate
wanted
Expected Belief

• Global knowledge

80
The Semantic Web - Trust Scenario
http//www.w3c.it/talks/ICTP/slide50-0.htm
81
FOAF Network
Reputation Systems
Y. Yesha
island
Kagal
source
Page Rank
J. Golbeck
knows
L. Ding
Citeseer Rank
H. Chen
J. Hendler
P. Kolari
knows
knows
F. Perich
T. Finin
Golbecks Trust Network
A. Joshi
Trust Inference FOAF(F) DBLP(D) F D Page
Rank Citeseer Golbecks Trust Unified
hub
sink
Peng
Ding
Yesha
Kolari
Finin
Kagal
Joshi
Avancha
Chen
Chakrabarty
Perich
DBLP Network
82
FOAF Network
Reputation Systems
J. Golbeck
source
Google PageRank
knows
Citeseer Rank
L. Ding
J. Hendler
H. Chen
P. Kolari
knows
F. Perich
knows
A. Joshi
T. Finin
Kagal
Golbecks Trust Network
sink
hub
island
mapTo
Y. Peng
L. Ding
6
1
28
A. Sheth
T. Finin
A. Joshi
L. Kagal
1
5
co-author
H. Chen
M. P. Singh
F. Perich
DBLP Coauthor Network