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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 Paper at http://bit.ly/k-pct 1
Find a set of entities (in the world) that explain a given set of sensor observations 2
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) 3
http://linkedsensordata.com 4
Semantic Sensor Network (SSN) Ontology http://www.w3.org/2005/Incubator/ssn/wiki/ 5
Parsimonious Covering Theory (PCT) Web Ontology Language (OWL) minimize explanations tractable degrade gracefully Web reasoning Convert PCT to OWL 6
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" 7
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) 8
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 ] ,[object Object],Goal Translate P into OWL, o(P), such that o(P) ⊧ Δ 9
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 10
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 11
Ohio Center of Excellence on  Knowledge-Enabled Computing (Kno.e.sis) thank you, and please visit us at http://semantic-sensor-web.com Kno.e.sis– Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, Ohio, USA 12

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Representation of Parsimonious Covering Theory in OWL-DL

  • 1. 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 Paper at http://bit.ly/k-pct 1
  • 2. Find a set of entities (in the world) that explain a given set of sensor observations 2
  • 3. 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) 3
  • 5. Semantic Sensor Network (SSN) Ontology http://www.w3.org/2005/Incubator/ssn/wiki/ 5
  • 6. Parsimonious Covering Theory (PCT) Web Ontology Language (OWL) minimize explanations tractable degrade gracefully Web reasoning Convert PCT to OWL 6
  • 7. 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" 7
  • 8. 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) 8
  • 9.
  • 10. 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 10
  • 11. 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 11
  • 12. Ohio Center of Excellence on Knowledge-Enabled Computing (Kno.e.sis) thank you, and please visit us at http://semantic-sensor-web.com Kno.e.sis– Ohio Center of Excellence in Knowledge-enabled Computing Wright State University, Dayton, Ohio, USA 12