Using Rules with Ontologies in The Semantic Web
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Using Rules with Ontologies in The Semantic Web

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Using Rules with Ontologies in The Semantic Web Using Rules with Ontologies in The Semantic Web Presentation Transcript

  • Using Rules with Ontologies in the Semantic Web Chimezie Ogbuji Thoracic and Cardiovascular Surgery Cleveland Clinic Foundation July 25 th , 2006 Presented to W3C HCLSIG ACPP Group
  • Rule Languages
    • RuleML
    • SWRL
    • Notation 3 (N3)
  • Semantic Web Reasoners
    • Closed World Machine (CWM)
    • Euler
    • Pychinko
    • RDFEngine
    • Jena
    • A Rule consists of a body (antecedent) and a head (consequent)
    • In N3:
      • {?X :has ?body. ?X :has ?head. :body log:implies :head } => {?X a :Rule }
    • In SWRL’s abstract syntax:
      • Implies(Antecedent(has(I-variable(X), I-variable(body) has(I-variable(X), I-variable(head)) log:Implies(I-variable(body), I-variable(head))) Consequent(Rule(I-variable(X)))
    Using Rules to Describe Rules
    • Logic Programming
      • Production systems (RETE, etc.)
      • Prolog
      • SQL
    • Horn-clause Logic
      • Efficient theorem proving, and reasoning
    Origin / Background of Rules
    • Kurt Cagle
      • “ You can never eliminate complexity from a system, you can only move it from place to place”
    • Important point regarding how / why rules are used with ontology languages
    Complexity and Abstraction
    • Description Logics (DL)
      • Strict subset of FOL with decidability in mind
    • DL are more palatable and (therefore) more ubiquitous
    • Ontology language constructs correspond to DL constructs
    • Some DL (ontology) reasoning can be done via explicit rules
    DL Semantics as Analogy for Rules
    • Transitive Roles
      • {?P a owl:TransitiveProperty. ?X ?P ?Y. ?Y ?P ?Z } => {?X ?P ?Z}
    • Class inclusion
      • {?B rdfs:subClassOf ?C. ?A rdfs:subClassOf ?B} => {?A rdfs:subClassOf ?C}.
    • Inverse Roles
      • {?P owl:inverseOf ?Q. ?S ?P ?O} => {?O ?Q ?S}.
    • Functional Restrictions
      • {?P a owl:FunctionalProperty. ?S ?P ?X. ?S ?P ?Y} => {?X owl:sameAs ?Y}.
    Some DL Semantics as N3 Rules
    • Certain implications cannot be expressed in DL:
      • “ Individuals who live and work at the same location are ‘Home Workers’”
    • In N3:
      • {?X :work ?Y. ?X :live ?Z. ?Z :located ?W. ?Y :located ?Y} => {?X a :HomeWorker}
    Restriction on Expressivenes
    • Specific logical restrictions (Horn logic) make it difficult to express certain statements:
      • “ Every person has a father (known or unknown)”
    • However, this is straight forward in OWL (and Description Logics):
      • :Person a owl:Class;
      • rdfs:subClassOf [
      • a owl:Restriction;
      • owl:onProperty :father;
      • owl:cardinality “1”.
      • ]
    Restriction on Expressiveness (Cont.)
    • Given:
      • Set of rules
      • Set of facts
    • Backward chaining is goal-oriented:
      • Question: can a fact be inferred from the rules and existing facts?
    • Forward chaining exhaustively infers new facts from the rules
      • The resulting facts combined with the original facts are often referred to as the ‘closure’
    Rule Inference Methods
    • Logic Programming reasoners and algorithms are more mature
      • RETE algorithm for production (forward chaining) systems
      • Euler cycle detection for backward chaining inference
    • Logic Programming systems are at the mercy of the explicit rules
    Concerns with Reasoning
    • Kurt Cagle
      • “ You can never eliminate complexity from a system, you can only move it from place to place”
    • DL abstracts Knowledge Representation at the expense of the reasoning mechanism.
      • DL reasoners are implemented to support only a limited kind of inference: class subsumption and consistency detection.
    Complexity and Abstraction (Revisited)
    • Use DL semantics where the domain falls nicely into Categories / Roles and decidability is an issue
    • Use rules everywhere else
    • The combination covers the full spectrum of expressiveness and decidability
    • Including both in the thought process improves Knowledge Engineering
    • Some DL semantics can be expressed as rules to take advantage of efficient pattern matching algorithms
    Compromise
    • Description Logic Programs: Combining Logic Programs with Description Logic
      • http://citeseer.ist.psu.edu/grosof03description.html
    • Description Logic Complexity Navigator
      • http://www.cs.man.ac.uk/~ezolin/logic/complexity.html
    • Web Ontology Reasoning with Logic Databases
      • http://www.ubka.uni-karlsruhe.de/vvv/2004/wiwi/2/2.pdf
    • Euler’s RDF Plus OWL N3 Rules
      • http://www.agfa.com/w3c/euler/rpo-rules.n3
    • Description Logics as Ontology Languages for the Semantic Web
      • http://citeseer.ist.psu.edu/baader03description.html
    References