Uncertainty and Semantic web

1
Uncertainty and Semantic web

• Jennifer Sleeman

2
Agenda

• Define uncertainty
• Provide background
• Show areas of research
• Highlight various approaches
• Provide a demonstration of Pronto

3
Definition - Uncertainty

• Knowledge can be inaccurate or incomplete
• Knowledge can be imprecise or fuzzy
• .leads to uncertainty

4
Definition - Uncertainty

• Machine-readable information
• Applications that work with random information
  (image processing, geospatial, information
  retrieval, etc.)
• Ontology concept definitions
• Vague concepts
• Tall, Small, Big, .
• Green, Blue, .
• Few, Many, .
• Semantic web services
• .work with uncertainty

5
Background Description Logic Naming Conventions
Taken from Wikipedia 12.
6
Is representing uncertainty necessary?

• Tim Berner-Lee rejection of uncertainty
• Not necessary 7
• Scalability issues 7

7
Can you describe knowledge using a monotonic
bivalent language7?
8
What about grey?
Uncertainty
9
Is it necessary?
Taken from 5 presented at the URSW 2008.
10
General Approaches to Uncertainty and Semantic
Web

• Incomplete/Distorted knowledge 1
• Possibility degrees alternatives
• Inability to define concepts precisely 1
• Degree of truth
• Conflicting alternatives 1
• Degree of probability
• According to 1, since how we solve uncertainty
  problems depends upon the domain, it is hard to
  define a single language extension.

11
Areas of Research (based upon 2007/2008 URSW
Conference agendas)

• Extending Semantic Web to support uncertainty
• Fuzzy theory
• Probability theory
• Uncertainty and Ontologies
• Uncertainty and Web Services

12
Extending the Semantic Web

• Extend Semantic Web languages to support
  probabilistic, possibilistic, and fuzzy reasoning
• Can be at the ontology layer or the rules layer
• Within the ontology layer proposals for
• Syntax and Semantics
• Logical Formalisms

13
Fuzzy Theory

• In classical set theory, the membership of
  elements in a set is assessed in binary terms
  according to a bivalent condition an element
  either belongs or does not belong to the set. By
  contrast, fuzzy set theory permits the gradual
  assessment of the membership of elements in a
  set this is described with the aid of a
  membership function valued in the real unit
  interval 0, 110

14
Fuzzy Approaches

• Extending languages such as OWL with fuzzy
  extensions
• Extending Description Logic with fuzzy extensions
• If a language is extended, one must provide a way
  to support reasoning of the language with the
  fuzzy extension

15
Rules and Uncertainty

• Rules Interchange Format
• Rules Markup Language
• For representing/interchanging rules
• Attempt to provide ways to represent various
  types of uncertainty 1
• Not as much recent attention as ontology layer
• fuzzy RuleML defines way to specify membership
  degree 1
• Example

Taken from 1.
16
Fuzzy RDF

• Extends syntax and semantics of RDF
• Triple extended to support real number on the
  interval 0,1
• n s p o 13
• Interpretation
• Subject, object has degree of membership to
  extension of predicate 13
• Satisfies statement if
• Membership degree of subject, object to the
  extension of the predicate is gt to n 13

17
Fuzzy RDF

• RDFS extended
• Class extensions are fuzzy sets of domain
  elements 13
• Domains are fuzzy and their assignment to
  properties can also be fuzzy 13
• Inference engines can be extended to support such
  fuzziness

18
Fuzzy Description Logic

• Fuzzy
• One such proposal
• Solve problem of representing and reasoning of
  fuzzy concepts
• With concrete domains reasoning
  using concrete data types
• With fuzzy version domains are fuzzy
• Modifiers are supported (very, slightly, etc.)
  12

19
Fuzzy Description Logic
Non-fuzzy Concrete Domain
Concrete Fuzzy Domain
Taken from 12.
20
Fuzzy Description Logic

• Interpretations are fuzzy
• From satisfied/unsatisfied to a degree of truth
  0,1
• Satisfiability of fuzzy axiom given fuzzy
  interpretation 12
• Fuzzy axiom a logical consequence of a knowledge
  base iff every model in the knowledge base
  satisfies the fuzzy axiom 12
• Reasoning a problem
• Computationally no calculus exists to check for
  satisfiability of a fuzzy knowledge model 12

21
Fuzzy OWL

• Extension of OWL
• Example (describing the safety of a location)
• Without fuzzy, the location is either safe or not
  safe
• With fuzzy, the location is safe to a degree
• Classes and properties are fuzzy
• A class is considered a fuzzy set 1
• A property is a fuzzy relation over a set 1

22
Fuzzy OWL

• Requires extension of to map OWL
  entailment to satisfiability 4
• Reasoning changes in that when concepts are
  represented as nodes in forest-like
  representations, a membership degree is
  associated with each node indicating it belongs
  to a concept 4
• Degrees added to OWL facts

23
Fuzzy OWL
Taken from 4.
24
Probability Theory

• ..the central objects of probability theory are
  random variables, stochastic processes, and
  events mathematical abstractions of
  non-deterministic events or measured quantities
  that may either be single occurrences or evolve
  over time in an apparently random fashion 11

25
PR-OWL

• Developed as an extension to OWL (basically an
  upper ontology)
• Represents complex Bayesian models 21
• Uses MEBN logic rather than extending OWL
• A first order Bayesian logic 21
• Consists of entities and attributes
• Attributes about entities and relationships to
  each other MEBN fragments (MFrag) 21
• Represent conditional probability distribution
  21
• MFrags organized into MEBN Theories (MTheories)
  21
• Collectively satisfy consistency constraints 21
• Goal
• Provide a way to support Bayesian models

26
PR-OWL
Taken from 21.
27
BayesOWL

• Express OWL ontologies as Bayesian networks by
  means of rules
• For each node, a conditional probability table
  (CPT) is constructed 15
• All subject and object classes translated into
  concept nodes 15
• Arc drawn between 2 concept nodes if the 2
  classes are related by predicate 15
• Direction based on class hierarchy
• L-Nodes generated during translation to represent
  OWL logical operators
• True/false value for each node indicates whether
  the instance belongs to the concept
• CPTs are approximated using the iterative
  proportional fitting procedure (IPFP) 15
• Restricted currently to OWL-DL taxonomies 15
• Goals
• Support ontology reasoning using probabilistic
  approach
• Support ontology mapping

28
BayesOWL
rdfssubClassOf
owlintersectionOf
owlunionOf
owlcomplementOf owlequivalentClass
owldisjointWith
Taken from 15.
29
BayesOWL

• DAG constructed
• CPTs for L-Nodes specified
• Concept nodes approximated using D-IPFP

Taken from 15.
30
BayesOWL

• Reasoning Support 15
• Concept satisfiability
• Concept overlapping
• Concept subsumption
• Extensions to OWL to support probabilistic
  representation 15
• PriorProb
• CondProb
• Concept Mapping 15

31
BayesOWL
Extensions to OWL Taken from 15.
32
Pronto

• Non-monotonic probabilistic DL reasoner
• Built on top of Pellet
• Uses P-SHIQ(D) formalism 8
• Expressing uncertain axioms
• Syntax based upon Lukasiewiczs conditional
  constraints 8
• Probabilistic Reasoning
• Lehmanns lexicographic entailment 8
• Represents uncertain ontological knowledge and
  reasoning 8
• Capable of representing uncertainty in both ABox
  and TBox axioms 8
• All inferences are done in a totally logical
  way (no translation) 8
• Uses OWL 1.1 axiom annotations to associate
  probability intervals with uncertain OWL axioms
  8
• Doesnt scale beyond 15 generic (TBox)
  conditional constraints 9

33
Pronto

• Conditional constraints
• (DC)l,u
• C and D concepts in P-SHIQ(D)
• l,u closed interval within 0,1
• Supports overriding
• Can handle certain probabilistic conflicts
• Flying birds/penguin problem
• Pronto allows more specific constraints to
  override more generic ones 9
• if Pronto knows that Tweety is a Penguin and
  Penguin is a subclass-of Bird, it will override
  the constraint (FlyingObjectBird)0.91.0 by
  (FlyingObjectPenguin)0.00.05 and correctly
  entail Tweety(FlyingObjectowlThing)0.00.05.
  9

34
Uncertainty and Ontologies - Mapping

• Mapping a problem
• Existing approaches - combination of syntactic
  and semantic measures 18, use machine learning,
  or linguistics and natural language processing
  15
• Quality varies depending upon domain 18
• Wang argues without use of a thesaurus,
  inaccuracies will occur 22
• Problem
• When mapping a concept from ontology A to
  ontology B there isnt always a single concept
  match but rather a number of concepts that match
  to some degree

35
Uncertainty and Ontologies - Mapping

• A proposed truth theory solution based on the
  following 18
• Dempster-Shafer, uncertain reasoning over
  potential mappings
• Evidence Theory
• Similarity matrix comparing all
  concepts/properties
• Similarity measure of a concept between O1 and O2
• DS combines evidence learned to form new belief
• Promising approach
• Multi-agent ontology mapping framework 18
• Not domain dependent
• Doesnt require large amounts of training data

36
Uncertainty and Ontologies - Mapping

• A proposed solution by Wang 22
• ACAOM
• Uses WordNet to calculate similarities for node
  names
• Name based mapping
• Instance strategy
• More semantics more feasible to match
• Documents assigned to nodes
• Uses vector space models to rank matches

37
Uncertainty and Ontologies - Mapping

• BayesOWL 15 also proposed a solution
• Argue that existing similarity approaches will
  not work
• If degree of similarity is not present in both
  concepts being matched 15
• If concept itself is fuzzy 15
• Uses BayesOWL and belief propagation between BNs
  15
• Ontologies are first translated into BNs 15
• Use probabilistic evidence reasoning to determine
  match 15

38
Uncertainty and Ontologies An Ontology of
Uncertainty

• Proposed by the W3C UR3W-XG group
• Provides a vocabulary for representing different
  types of uncertainty
• Was a good start but refinement needed 20
• Strategy to use such an ontology as a way to
  drive a reasoner
• Open issue coordination of reasoning of
  different uncertainty models in knowledge base
  19
• Uses SWRL rules to assign uncertainty to each
  relation 19

39
Uncertainty and Ontologies An Ontology of
Uncertainty
Taken from 20.
40
Uncertainty and Web Services

• Service discovery what is best service for
  request?
• Matching goal to service
• Brokers used for filtering
• Semantic Web Service Framework
• Semantic Web Service Language
  concepts/descriptions 17
• Semantic Web Service Ontology conceptual model
  17
• It is argued that current frameworks use first
  order and description logics and goal
  capabilities are based on subsumption checking
  or query-answering16
• Proposed approach uses Incident Calculus 16

41
Demo - Pronto

• Pronto Example Breast Cancer Risk Models
• Models 2 types of risks absolute and relative
• Combining risk factors to determine likelihood of
  breast cancer for a woman 8
• Distinction between known and inferred
• Pronto uses an ontology for knowledge
• Uses probabilistic statements to enable
  computable inferencing 8
• The probabilistic statements complement the OWL
  syntax

42
Demo - Pronto

• Risk factors relevant to breast cancer are
  subclasses of RiskFactor
• Categories of women that have certain risk
  factors are subclasses of WomanWithRiskFactors
• Women with risk of developing cancer subclass
  WomanUnderBRCRisk
• The goal
• Compute the probability that a certain woman is
  an instance of some WomanUnderBRCRisk subclass
  given that she is an instance of some
  WomanWithRiskFactors subclass 8
• Infer generic probabilistic subsumption between
  classes under WomanUnderBRCRisk and under
  WomanWithRiskFactors 8
• Conditional constraints are used to represent
  uncertain background knowledge using the OWL
  1.1 axiom annotations 8
• The demo defines constraints to express how risk
  factors influence the risk of developing cancer
  8
• Pronto combines the factors and computes the
  probability that a woman is an instance of a
  subclass of WomanUnderBRCRisk

43
Demo - Pronto

• ltowlObjectProperty rdfabout"hasRiskFactor"gt
• ltrdfsdomain rdfresource"Person"/gt
• ltrdfsrange rdfresource"RiskFactor"/gt
• lt/owlObjectPropertygt
• ltowlClass rdfabout"WomanTakingEstrogen"gt
• ltowlequivalentClassgt
• ltowlRestrictiongt
• ltowlonProperty
  rdfresource"hasRiskFactor"/gt
• ltowlsomeValuesFrom
  rdfresource"Estrogen"/gt
• lt/owlRestrictiongt
• lt/owlequivalentClassgt
• ltrdfssubClassOf rdfresource"Woman"/gt
• lt/owlClassgt
• Taken from http//clarkparsia.com/pronto/cancer_ra
  .owl

44
Demo - Pronto

• ltowlClass rdfabout"WomanWithRiskFactors"gt
• ltowlequivalentClassgt
• ltowlClassgt
• ltowlintersectionOf
  rdfparseType"Collection"gt
• ltrdfDescription
  rdfabout"Woman"/gt
• ltowlRestrictiongt
• ltowlonProperty
  rdfresource"hasRiskFactor"/gt
• ltowlsomeValuesFrom
  rdfresource"RiskFactor"/gt
• lt/owlRestrictiongt
• lt/owlintersectionOfgt
• lt/owlClassgt
• lt/owlequivalentClassgt
• ltrdfssubClassOf rdfresource"Woman"/gt
• lt/owlClassgt
• Taken from http//clarkparsia.com/pronto/cancer_ra
  .owl

45
Demo - Pronto

• ltowlClass rdfabout"WomanAgedUnder50"gt
• ltowlequivalentClassgt
• ltowlClassgt
• ltowlintersectionOf
  rdfparseType"Collection"gt
• ltrdfDescription
  rdfabout"Woman"/gt
• ltowlRestrictiongt
• ltowlonProperty
  rdfresource"hasAge"/gt
• ltowlsomeValuesFrom
  rdfresource"AgeUnder50"/gt
• lt/owlRestrictiongt
• lt/owlintersectionOfgt
• lt/owlClassgt
• lt/owlequivalentClassgt
• ltrdfssubClassOf rdfresource"WomanWithR
  iskFactors"/gt
• lt/owlClassgt
• Taken from http//clarkparsia.com/pronto/cancer_ra
  .owl

46
Demo - Pronto

• ltowlClass rdfabout"WomanUnderAbsoluteBRCRisk"
  gt
• ltowlequivalentClassgt
• ltowlClassgt
• ltowlintersectionOf
  rdfparseType"Collection"gt
• ltrdfDescription
  rdfabout"Woman"/gt
• ltowlRestrictiongt
• ltowlonProperty
  rdfresource"hasRisk"/gt
• ltowlsomeValuesFrom
  rdfresource"AbsoluteBRCRisk"/gt
• lt/owlRestrictiongt
• lt/owlintersectionOfgt
• lt/owlClassgt
• lt/owlequivalentClassgt
• lt/owlClassgt
• Taken from http//clarkparsia.com/pronto/cancer_ra
  .owl

47
Demo - Pronto

• ltowlClass rdfabout"WomanUnderBRCRisk"gt
• ltowlequivalentClassgt
• ltowlClassgt
• ltowlintersectionOf
  rdfparseType"Collection"gt
• ltrdfDescription
  rdfabout"Woman"/gt
• ltowlRestrictiongt
• ltowlonProperty
  rdfresource"hasRisk"/gt
• ltowlsomeValuesFrom
  rdfresource"BRCRisk"/gt
• lt/owlRestrictiongt
• lt/owlintersectionOfgt
• lt/owlClassgt
• lt/owlequivalentClassgt
• lt/owlClassgt
• Taken from http//clarkparsia.com/pronto/cancer_ra
  .owl

48
Demo - Pronto

• ltowlClass rdfabout"WomanUnderIncreasedBRCRisk"
  gt
• ltowlequivalentClassgt
• ltowlClassgt
• ltowlintersectionOf
  rdfparseType"Collection"gt
• ltowlRestrictiongt
• ltowlonProperty
  rdfresource"hasRisk"/gt
• ltowlsomeValuesFrom
  rdfresource"IncreasedBRCRisk"/gt
• lt/owlRestrictiongt
• ltrdfDescription
  rdfabout"WomanUnderBRCRisk"/gt
• lt/owlintersectionOfgt
• lt/owlClassgt
• lt/owlequivalentClassgt
• lt/owlClassgt
• Taken from http//clarkparsia.com/pronto/cancer_ra
  .owl

49
Demo - Pronto

• ltowlClass rdfabout"WomanUnderLifetimeBRCRisk"gt
  
• ltowlequivalentClassgt
• ltowlClassgt
• ltowlintersectionOf
  rdfparseType"Collection"gt
• ltrdfDescription
  rdfabout"Woman"/gt
• ltowlRestrictiongt
• ltowlonProperty
  rdfresource"hasRisk"/gt
• ltowlsomeValuesFrom
  rdfresource"LifetimeBRCRisk"/gt
• lt/owlRestrictiongt
• lt/owlintersectionOfgt
• lt/owlClassgt
• lt/owlequivalentClassgt
• ltrdfssubClassOf rdfresource"WomanUnder
  AbsoluteBRCRisk"/gt
• lt/owlClassgt
• Taken from http//clarkparsia.com/pronto/cancer_ra
  .owl

50
Demo - Pronto

• ltowlClass rdfabout"WomanUnderModeratelyIncrea
  sedBRCRisk"gt
• ltowlequivalentClassgt
• ltowlClassgt
• ltowlintersectionOf
  rdfparseType"Collection"gt
• ltrdfDescription
  rdfabout"WomanUnderIncreasedBRCRisk"/gt
• ltowlRestrictiongt
• ltowlonProperty
  rdfresource"hasRisk"/gt
• ltowlsomeValuesFrom
  rdfresource"ModeratelyIncreasedBRCRisk"/gt
• lt/owlRestrictiongt
• lt/owlintersectionOfgt
• lt/owlClassgt
• lt/owlequivalentClassgt
• ltrdfssubClassOf rdfresource"WomanUnder
  IncreasedBRCRisk"/gt
• ltowldisjointWith rdfresource"WomanUnde
  rStronglyIncreasedBRCRisk"/gt
• lt/owlClassgt
• Taken from http//clarkparsia.com/pronto/cancer_ra
  .owl

51
Demo - Pronto

• ltowlClass rdfabout"WomanUnderModeratelyReduce
  dBRCRisk"gt
• ltowlequivalentClassgt
• ltowlRestrictiongt
• ltowlonProperty
  rdfresource"hasRisk"/gt
• ltowlsomeValuesFrom
  rdfresource"ModeratelyReducedBRCRisk"/gt
• lt/owlRestrictiongt
• lt/owlequivalentClassgt
• ltrdfssubClassOf rdfresource"WomanUnder
  ReducedBRCRisk"/gt
• ltowldisjointWith rdfresource"WomanUnde
  rStronglyReducedBRCRisk"/gt
• ltowldisjointWith rdfresource"WomanUnde
  rWeakelyReducedBRCRisk"/gt
• lt/owlClassgt
• Taken from http//clarkparsia.com/pronto/cancer_ra
  .owl

52
Demo - Pronto

• lt!--Lifetime absolute risk--gt
• lt!-- Any woman has a 12.3 risk of lifetime
  breast cancer --gt
• ltowl11Axiomgt
• ltrdfsubject rdfresource"Woman"/gt
• ltrdfpredicate rdfresource"rdfssubClas
  sOf"/gt
• ltrdfobject rdfresource"WomanUnderLifet
  imeBRCRisk"/gt
• ltprontocertaintygt00.123lt/prontocertaint
  ygt
• lt/owl11Axiomgt
• lt!-- If a woman has BRCA mutation, then the
  risk is beteen 30 and 85 --gt
• ltowl11Axiomgt
• ltrdfsubject rdfresource"WomanWithBRCAM
  utation"/gt
• ltrdfpredicate rdfresource"rdfssubClas
  sOf"/gt
• ltrdfobject rdfresource"WomanUnderLifet
  imeBRCRisk"/gt
• ltprontocertaintygt0.30.85lt/prontocertain
  tygt
• lt/owl11Axiomgt
  
• lt!-- If it's BRCA1 mutation, then the
  lifetime risk is between 60 and 80 --gt

53
Demo - Pronto

• lt!-- Age-related risk--gt
• ltowl11Axiomgt
• ltrdfsubject rdfresource"WomanAgedUnder
  20"/gt
• ltrdfpredicate rdfresource"rdfssubClas
  sOf"/gt
• ltrdfobject rdfresource"WomanUnderShort
  TermBRCRisk"/gt
• ltprontocertaintygt00.0005lt/prontocertain
  tygt
• lt/owl11Axiomgt
• ltowl11Axiomgt
• ltrdfsubject rdfresource"WomanAged2030"
  /gt
• ltrdfpredicate rdfresource"rdfssubClas
  sOf"/gt
• ltrdfobject rdfresource"WomanUnderShort
  TermBRCRisk"/gt
• ltprontocertaintygt00.004lt/prontocertaint
  ygt
• lt/owl11Axiomgt
• ltowl11Axiomgt
• ltrdfsubject rdfresource"WomanAged3040"
  /gt
• ltrdfpredicate rdfresource"rdfssubClas
  sOf"/gt
• ltrdfobject rdfresource"WomanUnderShort
  TermBRCRisk"/gt

54
Demo - Pronto

• ltowl11Axiomgt
• ltrdfsubject rdfresource"WomanAged4050"
  /gt
• ltrdfpredicate rdfresource"rdfssubClas
  sOf"/gt
• ltrdfobject rdfresource"WomanUnderShort
  TermBRCRisk"/gt
• ltprontocertaintygt00.025lt/prontocertaint
  ygt
• lt/owl11Axiomgt
• ltowl11Axiomgt
• ltrdfsubject rdfresource"WomanAged5060"
  /gt
• ltrdfpredicate rdfresource"rdfssubClas
  sOf"/gt
• ltrdfobject rdfresource"WomanUnderShort
  TermBRCRisk"/gt
• ltprontocertaintygt00.035lt/prontocertaint
  ygt
• lt/owl11Axiomgt
• ltowl11Axiomgt
• ltrdfsubject rdfresource"WomanAged6070"
  /gt
• ltrdfpredicate rdfresource"rdfssubClas
  sOf"/gt

55
Demo - Pronto

• lt!--owl11Axiomgt
• ltrdfsubject rdfresource"Julie"/gt
• ltrdfpredicate rdfresource"rdftype"/gt
• ltrdfobject rdfresource"WomanAged3040"/
  gt
• ltprontocertaintygt11lt/prontocertaintygt
• lt/owl11Axiomgt
• ltowl11Axiomgt
• ltrdfsubject rdfresource"Mary"/gt
• ltrdfpredicate rdfresource"rdftype"/gt
• ltrdfobject rdfresource"WomanWithBRCA1M
  utation"/gt
• ltprontocertaintygt11lt/prontocertaintygt
• lt/owl11Axiomgt
• ltowl11Axiomgt
• ltrdfsubject rdfresource"Ann"/gt
• ltrdfpredicate rdfresource"rdftype"/gt
• ltrdfobject rdfresource"WomanWithMother
  BRCAffected"/gt
• ltprontocertaintygt11lt/prontocertaintygt

56
Demo - Pronto

• ltowl11Axiomgt
• ltrdfsubject rdfresource"Helen"/gt
• ltrdfpredicate rdfresource"rdftype"/gt
• ltrdfobject rdfresource"PostmenopausalW
  oman"/gt
• ltprontocertaintygt11lt/prontocertaintygt
• lt/owl11Axiomgt
• ltowl11Axiomgt
• ltrdfsubject rdfresource"Helen"/gt
• ltrdfpredicate rdfresource"rdftype"/gt
• ltrdfobject rdfresource"WomanTakingEstr
  ogen"/gt
• ltprontocertaintygt11lt/prontocertaintygt
• lt/owl11Axiomgt
• ltowl11Axiomgt
• ltrdfsubject rdfresource"Helen"/gt
• ltrdfpredicate rdfresource"rdftype"/gt

57
Demo - Pronto

• ltowl11Axiomgt
• ltrdfsubject rdfresource"AshkenaziJewis
  hWoman"/gt
• ltrdfpredicate rdfresource"rdfssubClas
  sOf"/gt
• ltrdfobject rdfresource"WomanWithBRCAMu
  tation"/gt
• ltprontocertaintygt0.0250.025lt/prontocert
  aintygt
• lt/owl11Axiomgt
• ltowl11Axiomgt
• ltrdfsubject rdfresource"WomanWithBRCAM
  utation"/gt
• ltrdfpredicate rdfresource"rdfssubClas
  sOf"/gt
• ltrdfobject rdfresource"WomanUnderLifet
  imeBRCRisk"/gt
• ltprontocertaintygt0.30.85lt/prontocertain
  tygt
• lt/owl11Axiomgt

58
Demo - Pronto

• Running query (generic TBox conditional
  constraint) (CD)l,u 9
• entail http//clarkparsia.com/pronto/cancer_ra.owl
  AshkenaziJewishWoman http//clarkparsia.com/pront
  o/cancer_ra.owlWomanUnderLifetimeBRCRisk

59
Demo - Pronto

• Query entail
• Result 34 (WomanUnderLifetimeBRCRiskAshkenaziJe
  wishWoman)0.00750.123
• Explanation
• Explaining the generic constraint 34
  (WomanUnderLifetimeBRCRiskAshkenaziJewish
• Woman)0.00750.123
• Lower bound is because of
• 8 (WomanWithBRCAMutationAshkenaziJewishWoman)
  0.0250.025, 7 (WomanUnderLi
• fetimeBRCRiskWomanWithBRCAMutation)0.30.85
• Upper bound is because of
• 10 (WomanUnderLifetimeBRCRiskWoman)0.00.123
  
• Result computed in 6266ms

60
Want to learn more?

• Attend the 2009 URSW Conference
• http//c4i.gmu.edu/ursw/2009/
• Visit W3C Uncertainty Reasoning for the World
  Wide Web Incubator Group
• http//www.w3.org/2005/Incubator/urw3/
• Review presentations from last years conference
• http//c4i.gmu.edu/ursw/2008/
• Download Pronto
• http//pellet.owldl.com/pronto/
• Download FiRE
• http//www.image.ece.ntua.gr/nsimou/FiRE/

61
References

• 1 - Stoilos,Simou,Stamou,Kollias,Uncertainty
  and the Semantic Web, http//www.image.ece.ntua.g
  r/php/savepaper.php?id445, 2006, IEEE
• 2 2008 Conference, Uncertainty Reasoning for
  the Semantic Web, http//c4i.gmu.edu/ursw/2008/in
  dex.html
• 3 - 2007 Conference, Uncertainty Reasoning
  for the Semantic Web, http//c4i.gmu.edu/ursw/200
  7/index.html
• 4 - Stoilos,Stamou,Tzouvaras,Pan,Horrocks,
  Fuzzy OWL Uncertainty and the Semantic Web,
  http//www.image.ntua.gr/papers/398.pdf
• 5 - Lassila, Some Personal Thoughts on
  Semantic Web and Non-symbolic AI,
  http//c4i.gmu.edu/ursw/2008/talks/URSW2008_Keynot
  e_Lassila.pdf, 2008, ISWC
• 6 Williams,Bastin,Cornford,Ingram,
  Describing and Communicating Uncertainty within
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• 7 Sanchez, Fuzzy logic and semantic web,
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  lotsmtbZcZfaO7sigVtGqKXu-rrzl5HOw36UBTeTpdoEh
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• 17 Semantic Web Services Framework (SWSF)
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