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This research was supported by grants from the US NSF (0219699, 0639230) 1
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
A Tableau-based Federated Reasoning Algorithm for
Modular Ontologies
Jie Bao and Vasant Honavar
Artificial Intelligence Research Laboratory,
Department of Computer Science,
Iowa State University, Ames, IA 50011-1040, USA.
{baojie, honavar}@cs.iastate.edu
International Conference on Web Intelligence (WI 2006),
Hong Kong, China, Dec 21st, 2006
This research was supported by grants from the US NSF (0219699, 0639230) 2
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Outline
• Ontology and Description Logics (DL)
• Modular Ontology and Package-based DL
• Distributed Reasoning with P-DL
This research was supported by grants from the US NSF (0219699, 0639230) 3
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Description Logics (DL)
• A family of Knowledge Representation (KR) formalisms – About Concepts (Classes), Properties (Roles,
Relationships) and Individuals (Instances)
– With formal semantics and well-understood computational behavior (decidability and complexity)
• Example
Students are People Student ⊑ PeopleStudent ⊑ People
some Students attend Classes Student ⊑ ∃attends.ClassesStudent ⊑ ∃attends.Classes
Bob is a Student Student(Bob)Student(Bob)
Concept
Property
Individual
This research was supported by grants from the US NSF (0219699, 0639230) 4
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
DL as Ontology Language
• ALC: the basic DL
• Many extensions– Number restrictions: a core family has at least 1 child
– Role hierarchy: hasBrother is less general than hasSibling
– …
Conjunction Male ⊓⊓⊓⊓HumanMan :=
Disjunction Child := Boy ⊔⊔⊔⊔Girl
Negation Woman := Human ⊓ ¬¬¬¬Man
Exists Restrictions Human := ∃hasParent.Human
Universal Restrictions Human := ∀∀∀∀hasBrother.Man
This research was supported by grants from the US NSF (0219699, 0639230) 5
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
DL Semantics
• An interpretation I=<∆I,(.)I >
– Concept à subset of ∆I
– Role à binary relations over ∆I ∆I×
– Individual à elements of ∆I
• Interpretation function is extended to concept expressions
(.)I
This research was supported by grants from the US NSF (0219699, 0639230) 6
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
DL Model
• An interpretation I satisfies an subsumption iff
• A model of an ontology O is an interpretation that satisfies every axiom in O
CI ⊆DIC⊑D
Student ⊑ PeopleStudent ⊑ People
Student ⊑ ∃attends.ClassesStudent ⊑ ∃attends.Classes
Student(Bob)Student(Bob)
∃attends.Class
Class
Student, People,
Bob
x
attends
This research was supported by grants from the US NSF (0219699, 0639230) 7
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Outline
• Ontology and Description Logics (DL)
• Modular Ontology and Package-based DL
• Distributed Reasoning with P-DL
This research was supported by grants from the US NSF (0219699, 0639230) 8
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
One or Many Web Ontologies?
• One single, universal ontology ?
A formal “encyclopedia” of all
knowledge on the web
• Or multiple, inter-connected ontologies ?
This research was supported by grants from the US NSF (0219699, 0639230) 9
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Call or Modularity
• Decentralization
– Web is decentralized, so will be for ontologies
– No ontology can capture the “full” knowledge for Web
• Context
– Ontologies represent local points of view– E.g. People ontology: ¬Male⊑ Female (an individual who is not a
Male is a Female) – implicit context “people”
– If a University ontology reuses the People ontology, will a “University” be a Male or Female?
• Scalability (for reasoning)
– Naive approach: download and integrate all ontologies
– Problem 1: There may be millions of axioms involved
– Problem 2: Global knowledge may not be available, e.g. in P2P
This research was supported by grants from the US NSF (0219699, 0639230) 10
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Package-based DL (P-DL)
• P-DL: Package-based Description Logics– A formal modular ontology language
– Extend DL with organizational modules called “package”
• Basic Intuitions – Syntax: a module may reuse knowledge from other
modules by importing foreign terms
– Semantics: localized (each module has local interpretation) and contextualized (axioms has scoped meaning)
– Reasoning: allow a federation of local reasoners collaborate with each other based on their local knowledge.
This research was supported by grants from the US NSF (0219699, 0639230) 11
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
P-DL SyntaxPeople Package (P1)
University Package (P2)
¬Male ⊑ FemaleMan ⊑ People ⊓Male
Woman ⊑ People ⊓ Female
People, Man, Woman
ALCPC: ALC extended with concept importing
Student ⊑ PeopleFaculty ⊑ People
Class ⊑ ∃taughtBy.People ⊓ ∀taughtBy.FacultyCoEd ⊑ University ⊓ ∃hasStudent.Man ⊓ ∃hasStudent.Woman
This research was supported by grants from the US NSF (0219699, 0639230) 12
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
P-DL Semantics
• Each package has a local interpretation
• Individuals in different domains can be associated
by domain relations
Man, People, Male
Woman, People, Female
People, Male
People, Female
Class
People, Faculty
CoEd, University
Man, People
Woman, People
hasStudent
hasStudent
taughtBy
r12∆I1 ∆I2
This research was supported by grants from the US NSF (0219699, 0639230) 13
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
P-DL Semantics
• Domain relations are
– one-to-one and
– compositional consistent
University ⊑Male ⊔ Female
• An axiom is always kept in its context:
M
FU
CIj = rij(CIi)CIj = rij(CIi)
• For any concept i:C :
CIi CIj
This research was supported by grants from the US NSF (0219699, 0639230) 14
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Outline
• Ontology and Description Logics (DL)
• Modular Ontology and Package-based DL
• Distributed Reasoning with P-DL
This research was supported by grants from the US NSF (0219699, 0639230) 15
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Tableau
• A tableau represents a model of a DL ontology
• We can use “ABox” (assertion set) to represent tableau
Class
People, Faculty
CoEd, University
Man, People
Woman, People
hasStudent
hasStudent
taughtBy
x1
x2
x3
x4
x5
Concept AssertionsMan(x1), People(x1)Woman(x2), People(x2)Class(x3)Faculty(x4),People(x4)CoEd(x5), University(x5)
Role AssertionshasStudent(x5,x1)hasStudent(x5,x2)taughtBy(x3,x4)
This research was supported by grants from the US NSF (0219699, 0639230) 16
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Tableau Algorithm
• Satisfiability of a concept C w.r.t. a DL Ontology TBox (set of concept inclusions) O can be checked by constructing a
common model of C and O
EasyClass(x0)
(∃taughtBy.Student)(x0)taughtBy(x0,x1)Student(x1)
¬Faculty(x1)Class(x0)
(∀taughtBy.Faculty)(x0)Faculty(x1)
Student ⊑ ¬FacultyEasyClass ⊑ ∃taughtBy.StudentClass ⊑ ∀taughtBy.Faculty
EasyClass ⊑ Class
Check: Satisfability of EasyClass
Note: we simplify the presentation (and in some following slides) by omitting some
facts due to “TBox internalization”, e.g., (EasyClass ⊔ ¬Class)(x0)
This research was supported by grants from the US NSF (0219699, 0639230) 17
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
ALC Expansion
((C⊔D)⊓∃R.D)(x),¬C(x), (∀R.¬D)(x)
(C⊔D)(x),∃R.D(x)⊓
∃ R(x,y),D(y)
C(x)
Inconsistent
⊔
¬D(y)
Inconsistent
∀
Incremental Storage
D(x)
Choice!
This research was supported by grants from the US NSF (0219699, 0639230) 18
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Distributed Tableaux
• Distributed Reasoning with P-DL– Syntactically: no integration of ontology modules is
needed
– Semantically: no (materialized) global tableau (or model) is needed
• How to make it possible?– Instead of using a global reasoner (with access to full
knowledge), we use a federation of local reasoners, each for a package, with only local knowledge of that package.
– Local reasoners communicate with each other to create a distributed tableau (distributed ABox)
This research was supported by grants from the US NSF (0219699, 0639230) 19
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Distributed Tableau
Package A Package B(Virtually)
Integrated Ontology
(Virtually)
Global Tableau
Local ABox A Local ABox B
This research was supported by grants from the US NSF (0219699, 0639230) 20
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Example
Package A Package B
B1 ⊑ ¬B2
A1 ⊑ A2A2 ⊑ ∃RA.B1A2 ⊑ ∀RA.B2
A1(x0)
A2(x0),(∃RA.B1)(x0)
RA(x0,x1), B1(x1)
(∀RA.B2)(x0)
B2(x1)
¬ B2(x1)
B1(x1) , B2(x1)
⊥⊥⊥⊥
This research was supported by grants from the US NSF (0219699, 0639230) 21
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Messages
• A fact of the form C(x) or ¬C(x) may be shared by two local tableaux– C is an atomic concept name– We don’t allow role name importing, hence role instances are never
shared
• Destination of facts– C(x) or ¬C(x) will always be sent to the reasoner for the home
package of C (where C is defined)
• Termination with acyclic concept importing [Bao et al. CRR 2006]
– Subset blocking can be locally applied to avoid non-termination.• E.g. {C(x),D(x),C(y)} then y is blocked by x
– Synchronous reasoning: local expansions are blocked until a remote answer (clash or consistency) is returned (i.e., only one branch of ABox tree is under expanding at any time)
– Hence there is no cyclic message between local reasoners
This research was supported by grants from the US NSF (0219699, 0639230) 22
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Handle Cyclic Importing
• Cyclic Importing
• Difficulty
– How to ensure no cyclic messages or deadlock between local reasoners
– How to maximize the usage of computational resources by parallel, asynchronous reasoning: local reasoners
may work on different (search) branches simultaneously
Package A Package B
This research was supported by grants from the US NSF (0219699, 0639230) 23
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Handle Cyclic Importing (2)
• Key: different search branches are kept globally separated • Contact List: every node has one and only one contact node from each
local ABox tree. – Can be locally inherited
– Updated after receiving messages (only most recent contacts are kept)
• If a fact in node n of Tj is sent to tableau Ti, it is added to – lsti(n), if no local branches created since last message from lsti(n)
–
nA0
nA1nA2 nB0
nB1
nB2
lst= nA1
lst= nA1
nA0
nA1nA2 nB0
nB1
nB2
lst= nA1
lst= nA1
lst= nA1lst= nA1
This research was supported by grants from the US NSF (0219699, 0639230) 24
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Handle Cyclic Importing (2)
• Key: different search branches are kept globally separated • Contact List: every node has one and only one contact node from each
local ABox tree. – Can be locally inherited
– Updated after receiving messages (only most recent contacts are kept)
• If a fact in node n of Tj is sent to tableau Ti, it is added to – lsti(n), if no local branches created since last message from lsti(n)
– A new node under lsti(n), otherwise
nA0
nA1nA2
nA3
nB0
nB1
nB3nB2
lst= nA1
lst= nA1
nA0
nA1nA2
nA3
nB0
nB1
nB3nB2
lst= nA1
lst= nA1
This research was supported by grants from the US NSF (0219699, 0639230) 25
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Example 2
(A1⊓⊓⊓⊓¬¬¬¬A3 ⊓⊓⊓⊓(¬¬¬¬A1⊔⊔⊔⊔B1)⊓⊓⊓⊓(¬¬¬¬A2⊔⊔⊔⊔B2))(x)
Time 1 TA
Package A Package B
This research was supported by grants from the US NSF (0219699, 0639230) 26
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Example 2
(A1⊓¬A3 ⊓(¬A1⊔B1)⊓(¬A2⊔B2))(x)
Time 2
A1(x),¬A3(x),(¬¬¬¬A1⊔⊔⊔⊔B1)(x),(¬¬¬¬A2⊔⊔⊔⊔B2)(x)
TA
This research was supported by grants from the US NSF (0219699, 0639230) 27
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Example 2
(A1⊓¬A3 ⊓(¬A1⊔B1)⊓(¬A2⊔B2))(x)
Time 3
A1(x),¬A3(x),(¬A1⊔B1)(x),(¬¬¬¬A2⊔⊔⊔⊔B2)(x)
¬A1(x) B1(x)
TA
This research was supported by grants from the US NSF (0219699, 0639230) 28
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Example 2
(A1⊓¬A3 ⊓(¬A1⊔B1)⊓(¬A2⊔B2))(x)
Time 4
A1(x),¬A3(x),(¬A1⊔B1)(x),(¬¬¬¬A2⊔⊔⊔⊔B2)(x)
¬A1(x)
B1(x),(¬B1⊔A2⊔A3)(x),(¬¬¬¬B2⊔⊔⊔⊔A3)(x)
¬B1(x) A2(x) A3(x)
B1(x)
TA TB
B1(x)
This research was supported by grants from the US NSF (0219699, 0639230) 29
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Example 2
(A1⊓¬A3 ⊓(¬A1⊔B1)⊓(¬A2⊔B2))(x)
Time 5
A1(x),¬A3(x),(¬A1⊔B1)(x),(¬¬¬¬A2⊔⊔⊔⊔B2)(x)
¬A1(x)
B1(x),(¬B1⊔A2⊔A3)(x),(¬¬¬¬B2⊔⊔⊔⊔A3)(x)
¬B1(x) A3(x)
¬A2(x) B2(x)
A2(x)
A2(x)
B1(x)
TA TB
A2(x)
This research was supported by grants from the US NSF (0219699, 0639230) 30
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Example 2
(A1⊓¬A3 ⊓(¬A1⊔B1)⊓(¬A2⊔B2))(x)
Time 6
A1(x),¬A3(x),(¬A1⊔B1)(x),(¬A2⊔B2)(x)
¬A1(x)
B1(x),(¬B1⊔A2⊔A3)(x),(¬B2⊔A3)(x)
¬B1(x) A3(x)
A3(x) ¬B2(x)
¬A2(x) B2(x)
A2(x)
B1(x)
A2(x)
B2(x)
TATB
This research was supported by grants from the US NSF (0219699, 0639230) 31
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Example 2
(A1⊓¬A3 ⊓(¬A1⊔B1)⊓(¬A2⊔B2))(x)
Time 7
A1(x),¬A3(x),(¬A1⊔B1)(x),(¬A2⊔B2)(x)
¬A1(x)
B1(x),(¬B1⊔A2⊔A3)(x),(¬B2⊔A3)(x)
¬B1(x) A3(x)
A3(x) ¬B2(x)
¬A2(x) B2(x)
A2(x)
B1(x)
A2(x)
B2(x)
A3(x)
clash
A3(x)
TA TB
This research was supported by grants from the US NSF (0219699, 0639230) 32
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Example 2
(A1⊓¬A3 ⊓(¬A1⊔B1)⊓(¬A2⊔B2))(x)
Time 8 (Hide some unsuccessful branches)
A1(x),¬A3(x),(¬A1⊔B1)(x),(¬A2⊔B2)(x)
¬A1(x)
B1(x),(¬B1⊔A2⊔A3)(x),(¬¬¬¬B2⊔⊔⊔⊔A3)(x)
¬B1(x) A3(x)
B1(x)
A2(x)
A3(x)
TATB
A3(x)
clash
This research was supported by grants from the US NSF (0219699, 0639230) 33
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Summary
We presented a federated, asynchronous reasoning
algorithm for modular ontologies such that
• No global knowledge is required
• Cyclic concept name importing is allowed
• Reasoning can be performed in asynchronous,
peer-to-peer fashion
• Can handle both inter-module subsumption (like
DDL[Borgida and Serafini, 2002]) and roles with foreign
range (like E-Connections [Grau et al. 2004])
This research was supported by grants from the US NSF (0219699, 0639230) 34
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Ongoing Work
• Reasoning with expressive modular ontologies
– More expressive component languages
• ALC à SHOIQ
– More expressive semantic connections
• Concept importing à Concept + Role + Nominal importing
• Theoretical investigation
– Contextualized negation
– Locally closed world semantics
– Controlled axiom propagation (partial ontology reuse)
This research was supported by grants from the US NSF (0219699, 0639230) 35
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Thanks
This research was supported by grants from the US NSF (0219699, 0639230) 36
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Ontology
• Science of Being (Aristotle, Metaphysics, IV, 1)
• Some formal descriptions about
– A vocabulary
– Relations between terms in the vocabulary
People
Student
Class
Bob
• Ontology Languages: Frame Logics, Description
Logics,…
is a
attendless general than
This research was supported by grants from the US NSF (0219699, 0639230) 37
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Web Ontology Language
• OWL: a syntactical variation of the DL SHOIQ(D)
• Used to represent knowledge on the Semantic Web
Web Data
Meta Data
(Ontology)
P hDStudent(J ieBao)
P hDStudent ⊑ Graduate
Graduate ⊑ Student
Student ⊑ P eople
This research was supported by grants from the US NSF (0219699, 0639230) 38
Iowa State University Department of Computer Science
Artificial Intelligence Research Laboratory
Contextualized Negation
(¬C)Ij = rij(∆Ii)\rij(C
Ii)(¬C)Ij = rij(∆Ii)\rij(C
Ii)
(¬C)Ij = ∆Ij\rij(CIi)
Not