An Introduction to Description Logics.ppt

1、An Introduction to Description Logics,What Are Description Logics?,A family of logic based Knowledge Representation formalisms Descendants of semantic networks and KL-ONE Describe domain in terms of concepts (classes), roles (relationships) and individuals Distinguished by: Formal semantics (typical

2、ly model theoretic) Decidable fragments of FOL Closely related to Propositional Modal & Dynamic Logics Provision of inference services Sound and complete decision procedures for key problems Implemented systems (highly optimised),DL Architecture,Knowledge Base,Tbox (schema),Abox (data),Man Human u M

3、ale Happy-Father Man u 9 has-child Female u ,John : Happy-Father hJohn, Maryi : has-child,Inference System,Interface,Short History of Description Logics,Phase 1: Incomplete systems (Back, Classic, Loom, . . . ) Based on structural algorithms Phase 2: Development of tableau algorithms and complexity

4、results Tableau-based systems for Pspace logics (e.g., Kris, Crack) Investigation of optimisation techniques Phase 3: Tableau algorithms for very expressive DLs Highly optimised tableau systems for ExpTime logics (e.g., FaCT, DLP, Racer) Relationship to modal logic and decidable fragments of FOL,Lat

5、est Developments,Phase 4: Mature implementations Mainstream applications and Tools Databases Consistency of conceptual schemata (EER, UML etc.) Schema integration Query subsumption (w.r.t. a conceptual schema) Ontologies and Semantic Web (and Grid) Ontology engineering (design, maintenance, integrat

6、ion) Reasoning with ontology-based markup (meta-data) Service description and discovery Commercial implementations Cerebra system from Network Inference Ltd,Description Logic Family,DLs are a family of logic based KR formalisms Particular languages mainly characterised by: Set of constructors for bu

7、ilding complex concepts and roles from simpler ones Set of axioms for asserting facts about concepts, roles and individualsALC is the smallest DL that is propositionally closed Constructors include booleans (and, or, not), and Restrictions on role successors E.g., concept describing “happy fathers”

8、could be written:Man hasChild.Female hasChild.Male hasChild.(Rich Happy),DL Concept and Role Constructors,Range of other constructors found in DLs, including: Number restrictions (cardinality constraints) on roles, e.g., 3 hasChild, 1 hasMother Qualified number restrictions, e.g., 2 hasChild.Female,

9、 1 hasParent.Male Nominals (singleton concepts), e.g., Italy Concrete domains (datatypes), e.g., hasAge.(21), earns spends.Inverse roles, e.g., hasChild- (hasParent) Transitive roles, e.g., hasChild* (descendant) Role composition, e.g., hasParent o hasBrother (uncle),DL Knowledge Base,DL Knowledge B

10、ase (KB) normally separated into 2 parts: TBox is a set of axioms describing structure of domain (i.e., a conceptual schema), e.g.: HappyFather Man hasChild.Female Elephant Animal Large Grey transitive(ancestor)ABox is a set of axioms describing a concrete situation (data), e.g.: John:HappyFather :h

11、asChildSeparation has no logical significance But may be conceptually and implementationally convenient,OWL as DL: Class Constructors,XMLS datatypes as well as classes in 8P.C and 9P.C E.g., 9hasAge.nonNegativeInteger Arbitrarily complex nesting of constructors E.g., Person u 8hasChild.(Doctor t 9ha

12、sChild.Doctor),RDFS Syntax,E.g., Person u 8hasChild.(Doctor t 9hasChild.Doctor):,OWL as DL: Axioms,Axioms (mostly) reducible to inclusion (v) C D iff both C v D and D v C Obvious FOL equivalences E.g., C D x.C(x) D(x), C v D x.C(x) D(x),XML Schema Datatypes in OWL,OWL supports XML Schema primitive d

13、atatypes E.g., integer, real, string, Strict separation between “object” classes and datatypes Disjoint interpretation domain DD for datatypes For a datavalue d, dI DD And DD DI = ; Disjoint “object” and datatype properties For a datatype propterty P, PI DI DD For object property S and datatype prop

14、erty P, SI PI = ; Equivalent to the “(Dn)” in SHOIN(Dn),Why Separate Classes and Datatypes?,Philosophical reasons: Datatypes structured by built-in predicates Not appropriate to form new datatypes using ontology language Practical reasons: Ontology language remains simple and compact Semantic integr

15、ity of ontology language not compromised Implementability not compromised can use hybrid reasoner Only need sound and complete decision procedure for: dI1 dIn, where d is a (possibly negated) datatype,OWL DL Semantics,Mapping OWL to equivalent DL (SHOIN(Dn): Facilitates provision of reasoning servic

16、es (using DL systems) Provides well defined semantics DL semantics defined by interpretations: I = (DI, I), whereDI is the domain (a non-empty set) I is an interpretation function that maps: Concept (class) name A ! subset AI of DI Role (property) name R ! binary relation RI over DI Individual name

17、i ! iI element of DI,DL Semantics,Interpretation function I extends to concept expressions in the obvious way, i.e.:,Interpretation Example, = v, w, x, y, z AI = v, w, x BI = x, y RI = (v, w), (v, x), (y, x), (x, z): B = A u B =: A t B =9 R B =8 R B =9 R (9 R A) = 9 R : (A t B) =6 1 R A = 1 R A =,AI

18、,v,x,y,z,w,BI,DL Knowledge Bases (Ontologies),An OWL ontology maps to a DL Knowledge Base K = hT , Ai T (Tbox) is a set of axioms of the form: C v D (concept inclusion) C D (concept equivalence) R v S (role inclusion) R S (role equivalence) R+ v R (role transitivity) A (Abox) is a set of axioms of t

19、he form x 2 D (concept instantiation) hx,yi 2 R (role instantiation) Two sorts of Tbox axioms often distinguished “Definitions” C v D or C D where C is a concept name General Concept Inclusion axioms (GCIs) C v D where C in an arbitrary concept,Knowledge Base Semantics,An interpretation I satisfies

20、(models) an axiom A (I A): I C v D iff CI DI I C D iff CI = DI I R v S iff RI SI I R S iff RI = SI I R+ v R iff (RI)+ RI I x 2 D iff xI 2 DI I hx,yi 2 R iff (xI,yI) 2 RI I satisfies a Tbox T (I T ) iff I satisfies every axiom A in T I satisfies an Abox A (I A) iff I satisfies every axiom A in A I sa

21、tisfies an KB K (I K) iff I satisfies both T and A,Multiple Models -v- Single Model,DL KB doesnt define a single model, it is a set of constraints that define a set of possible models No constraints (empty KB) means any model is possible More constraints means fewer models Too many constraints may m

22、ean no possible model (inconsistent KB) In contrast, DBs (and frame/rule KR systems) make assumptions such that DB/KB defines a single model Unique name assumption Different names always interpreted as different individuals Closed world assumption Domain consists only of individuals named in the DB/

23、KB Minimal models Extensions are as small as possible,Example of Multiple Models,KB = KB = a:C, b:D, c:C, d:EKB = a:C, b:D, c:C, d:E, b:CKB = a:C, b:D, c:C, d:E, b:CD v CKB = a:C, b:D, c:C, d:E, b:CD v C, E v CKB = a:C, b:D, c:C, d:E, b:CD v C, E v C, d: C,I1: = v, w, x, y, z CI = v, w, y DI = x, y

24、EI = z aI = v bI = x cI = w dI = yI3: = v, w, x, y, z CI = v, w, y DI = x, y EI = z aI = v bI = y cI = w dI = z,I2: = v, w, x, y, z CI = v, w, y DI = x, y EI = z aI = v bI = x cI = w dI = zI4: = v, w, x, y, z CI = v, w, x, y DI = x, y EI = z aI = v bI = x cI = y dI = y,Example of Single Model,KB = K

25、B = a:C, b:D, c:C, d:EKB = a:C, b:D, c:C, d:E, b:CKB = a:C, b:D, c:C, d:E, b:CE v C,I: = I: = a, b, c, d CI = a, b, c DI = b EI = d aI = a bI = b cI = c dI = d,I: = a, b, c, d CI = a, c DI = b EI = d aI = a bI = b cI = c dI = dI: = a, b, c, d CI = a, b, c, d DI = b EI = d aI = a bI = b cI = c dI = d

26、,Inference Tasks,Knowledge is correct (captures intuitions) C subsumes D w.r.t. K iff for every model I of K, CI DI Knowledge is minimally redundant (no unintended synonyms) C is equivallent to D w.r.t. K iff for every model I of K, CI = DI Knowledge is meaningful (classes can have instances) C is s

27、atisfiable w.r.t. K iff there exists some model I of K s.t. CI ;Querying knowledge x is an instance of C w.r.t. K iff for every model I of K, xI 2 CIhx,yi is an instance of R w.r.t. K iff for, every model I of K, (xI,yI) 2 RIKnowledge base consistency A KB K is consistent iff there exists some model

28、 I of K,Single Model -v- Multiple Model,Multiple models: Expressively powerful Boolean connectives, including : and t Can capture incomplete information E.g., using t and 9 Monotonic Adding information preserves truth Reasoning (e.g., querying) is hard/slow Queries may give counter-intuitive results in some cases,Single model: Expressively weaker (in most respects) No negation or disjunction Cant capture incomplete information Nonmonotonic Adding information does not preserve truth Reasoning (e.g., querying) is easy/fast Queries may give counter-intuitive results in some cases,