Using Rules with Ontologies in the Semantic Web Chimezie Ogbuji Thoracic and Cardiovascular Surgery Cleveland Clinic Foundation July 25th, 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

Using Rules to Describe Rules 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)))

Origin / Background of Rules Logic Programming Production systems (RETE, etc.) Prolog SQL Horn-clause Logic Efficient theorem proving, and reasoning

Complexity and Abstraction 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

DL Semantics as Analogy for Rules 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

Some DL Semantics as N3 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}.

Restriction on Expressivenes 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 Expressiveness (Cont.) 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”. ]

Rule Inference Methods 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’

Concerns with Reasoning 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

Complexity and Abstraction (Revisited) 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.

Compromise 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

References 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