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PR-OWL: A Framework for Probabilistic Ontologies

PR-OWL: A Framework for Probabilistic Ontologies. by Paulo C. G. COSTA, Kathryn B. LASKEY George Mason University presented by Thomas Packer. Problem Area. Ontologies are useful: Machine usable description of shared knowledge Support inferences using classical logic

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PR-OWL: A Framework for Probabilistic Ontologies

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  1. PR-OWL: A Framework for ProbabilisticOntologies by Paulo C. G. COSTA, Kathryn B. LASKEY George Mason University presented by Thomas Packer PR-OWL

  2. Problem Area • Ontologies are useful: • Machine usable description of shared knowledge • Support inferences using classical logic • Probabilities are useful: • More effective merging (sharing?) of knowledge. • Support principled reasoning over noisy, uncertain, contradictory or incomplete knowledge. • Can we use both at the same time? PR-OWL

  3. Dealing with Incomplete Knowledge • What concept does the term “Washington” correspond to? • With limited prior knowledge, it has some probability of representing: • US Capital • State • Baseball team • New evidence (from context ) changes that distribution. • “Washington voiced strong objections to the proposed policy.” PR-OWL

  4. Probabilistic Ontologies We need / they present: • The beginning of a coherent framework • Formal definition • Extension of OWL consistent with formal definition (PR-OWL) PR-OWL

  5. Previous Approaches • Annotate objects and properties in an OWL ontology with probabilities. • Allows translation into Bayesian Network. • BNs have limited attribute-value representations. • Cannot represent probabilities dependent on more structure. • Cannot be used to infer probabilities of structures that are not explicit in the ontology. • Probabilistic extensions of DL. • Limited ability to represent constraints on the instances that can participate in a relationship. PR-OWL

  6. PR-OWL • Based on a probabilistic logic: MEBN. • MEBN: Multi-Entity Bayesian Networks • First-order Bayesian logic • Integrates first-order logic with probability theory. • Provides a logically coherent representation of uncertainty. • FOL: First-order logic • By far the most commonly used, studied and implemented logical system. • Logical basis for most current AI systems and ontology languages. PR-OWL

  7. MEBN • Represents a coherent probability distribution: • Probability of any option is between 0 and 1. • Probability of all options sum to 1. • Can reduce to classical logic (all probabilities are exactly 0 or 1). • Entities, attributes and relationships are described with conditional probability distributions. • Entity X has identity x1 with probability p1 given the identities of related entities. (MFrags) • Collectively provides a joint probability distribution. (MTheory) • Bayes theorem provide a mathematical foundation for learning and inference. PR-OWL

  8. MEBN Intentions • Upper ontology (meta-model?) • A proposal for a W3C Standard • A set of classes, subclasses and properties that collectively form a framework for building probabilistic ontologies. PR-OWL

  9. Main Elements of the PR-OWL Upper Ontology PR-OWL

  10. How to Use PR-OWL • Import into any OWL editor an OWL file containing PR-OWL classes, subclasses and properties. • Construct domain-specific concepts using the PR-OWL definitions to represent uncertainty about their attributes and relationships. • Define concept instances about which probabilities can be expressed. (Everything need not be probabilistic.) • Feed probabilistic ontology into a probabilistic reasoner to answer probabilistic queries. PR-OWL

  11. Conclusion (Strengths) • Compelling approach to combining probabilities and ontologies. PR-OWL

  12. Conclusion (Weaknesses) • No formal evaluation. • Not standardized. • No supporting tools. PR-OWL

  13. Conclusion • Good start. • Probabilistic ontologies are useful enough that I believe they will eventually become standardized. • This and other research will help push the SW community toward that goal. PR-OWL

  14. Questions PR-OWL

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