Representation of Parsimonious Covering Theory in OWL-DL
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Representation of Parsimonious Covering Theory in OWL-DL

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Presentation given at OWLED 2011 (http://www.webont.org/owled/2011/), paper can be found here: http://www.knoesis.org/library/resource.php?id=1546

Presentation given at OWLED 2011 (http://www.webont.org/owled/2011/), paper can be found here: http://www.knoesis.org/library/resource.php?id=1546

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Representation of Parsimonious Covering Theory in OWL-DL Presentation Transcript

  • 1. 1
  • 2. OWL Experiences and Directions (OWLED 2011)
    Representation of Parsimonious Covering Theory
    in OWL-DL
    Cory Henson, KrishnaprasadThirunarayan, AmitSheth, Pascal Hitzler
    Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)
    Wright State University, Dayton, Ohio, USA
    2
  • 3. Find a set of entities (in the world) that
    explain a given set of sensor observations
    3
  • 4. Characteristics of a Solution
    Handle incomplete information (graceful degradation)
    Minimize explanations with additional information (anti-monotonic)
    Reason over data on the Web (i.e., RDF on LOD)
    Scalable (tractable)
    4
  • 5. http://linkedsensordata.com
    5
  • 6. Semantic Sensor Network (SSN) Ontology
    http://www.w3.org/2005/Incubator/ssn/wiki/
    6
  • 7. Parsimonious Covering Theory (PCT)
    Web Ontology
    Language (OWL)
    minimize
    explanations
    tractable
    degrade gracefully
    Web reasoning
    Convert PCT to OWL
    7
  • 8. Parsimonious Covering Theory
    Goal is to account for observed symptoms with plausible explanatory hypotheses (abductive logic)
    Driven by background knowledge modeled as a bipartite graph causally linking disorders to manifestations
    disorder
    manifestation
    causes
    m1
    d1
    m2
    d2
    m3
    d3
    m4
    explanation
    observations
    YunPeng, James A. Reggia, "Abductive Inference Models for Diagnostic Problem-Solving"
    8
  • 9. PCT Parsimonious Cover
    coverage: an explanation is a cover if, for each observation, there is a causal relation from a disorder contained in the explanation to the observation
    parsimony: an explanation is parsimonious, or best, if it contains only a single disorder (single disorder assumption)
    9
  • 10. Given
    PCT problem P is a 4-tuple ⟨D, M, C, Γ⟩
    D is a finite set of disorders
    M is a finite set of manifestations
    C is the causation function [C : D ⟶ Powerset(M)]
    Γ is the set of observations [Γ ⊆ M ]
    • Δ is a valid explanation (i.e., is a parsimonious cover)
    Goal
    Translate P into OWL, o(P), such that o(P) ⊧ Δ
    10
  • 11. disorders (D)
    for all d ∈ D, write d rdf:type Disorder
    ex: flu rdf:type Disorder
    cold rdf:type Disorder
    manifestations (M)
    for all m ∈ M, write m rdf:type Manifestation
    ex: fever rdf:type Manifestation
    headache rdf:type Manifestation …
    causes relations (C)
    for all (d, m) ∈ C, write d causes m
    ex: flu causes fever
    flu causes headache …
    PCT Background
    Knowledge in OWL
    disorder
    manifestation
    causes
    fever
    headache
    extreme exhaustion
    severe ache and pain
    flu
    mild ache and pain
    stuffy nose
    sneezing
    cold
    sore throat
    severe cough
    mild cough
    11
  • 12. observations (Γ)
    for mi∈ Γ, i =1 … n, write
    Explanation owl:equivalentClass
    causes value m1 and … causes value mn
    ex: Explanation owl:equivalentClass
    causes value sneezing and
    causes value sore-throat
    causes value mild-cough
    explanation (Δ)
    Δrdf:type Explanation, is deduced
    ex: cold rdf:type Explanation
    flu rdf:type Explanation
    PCT Observations and
    Explanations in OWL
    and
    12
  • 13. Ohio Center of Excellence on
    Knowledge-Enabled Computing (Kno.e.sis)
    thank you, and please visit us at
    http://semantic-sensor-web.com
    Knoesis – Ohio Center of Excellence in Knowledge-enabled Computing
    Wright State University, Dayton, Ohio, USA
    13