This document provides an overview of description logics, which are a family of formal knowledge representation languages. Description logics are based on semantic networks and modal logic. They allow modeling a domain in terms of concepts, roles, and individuals. Description logics have a formal semantics, decidable reasoning problems, and highly optimized implemented systems. The document describes the basics of description logics, including concept and role constructors, knowledge base structure and semantics, the DL family of languages, and applications of description logic systems.

1. Description Logic: A Formal
Approach to Knowledge
Representation

2. 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 (properties,
relationships) and individuals
• Distinguished by:
• Formal semantics (typically model theoretic)
• Decidable fragments of FOL (often contained in C2)
• Closely related to Propositional Modal & Dynamic Logics
• Closely related to Guarded Fragment
• Provision of inference services
• Decision procedures for key problems (satisfiability, subsumption, etc)
• Implemented systems (highly optimised)

3. DL Basics
• Concept names are equivalent to unary predicates
• In general, concepts equiv to formulae with one free
variable
• Role names are equivalent to binary predicates
• In general, roles equiv to formulae with two free variables
• Individual names are equivalent to constants
• Operators restricted so that:
• Language is decidable and, if possible, of low complexity
• No need for explicit use of variables
• Restricted form of 9 and 8 (direct correspondence with ◊ and [])
• Features such as counting can be succinctly expressed

4. DL System Architecture
Knowledge Base
Tbox (schema)
Abox (data)
Inference
System
Interface
Man ´ Human u Male
Happy-Father ´ Man u 9 has-child
Female u …
John : Happy-Father
hJohn, Maryi : has-child
John: 6 1 has-child

5. The DL Family
• Given DL defined by set of concept and role forming operators
• Smallest propositionally closed DL is ALC (equiv modal K(m))
• Concepts constructed using u, t, :, 9 and 8
• S often used for ALC with transitive roles (R+)
• Additional letters indicate other extension, e.g.:
• H for role inclusion axioms (role hierarchy)
• O for nominals (singleton classes, written {x})
• I for inverse roles
• N for number restrictions (of form 6nR, >nR)
• Q for qualified number restrictions (of form 6nR.C, >nR.C)
• E.g., ALC + R+ + role hierarchy + inverse roles + QNR = SHIQ
• Have been extended in many directions
• Concrete domains, fixpoints, epistemic, n-ary, fuzzy, …

6. DL Semantics
• Semantics defined by interpretations
• An interpretation I = (DI, ¢I), where
• DI 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 i ! iI element of DI

7. DL Semantics (cont.)
• Interpretation function ¢I extends to concept (and
role) expressions in the obvious way, e.g.:

8. DL Knowledge Base
• A DL Knowledge base K is a pair hT ,Ai where
• T is a set of “terminological” axioms (the Tbox)
• A is a set of “assertional” axioms (the Abox)
• Tbox axioms are of the form:
C v D, C ´ D, R v S, R ´ S and R+ v R
where C, D concepts, R, S roles, and R+ set of transitive
roles
• Abox axioms are of the form:
x:D, hx,yi:R
where x,y are individual names, D a concept and R a role

9. Knowledge Base Semantics
• An interpretation I satisfies (models) a Tbox axiom A (I ² A):
I ² C v D iff CI µ DI I ² C ´ D iff CI = DI
I ² R v S iff RI µ SI I ² R ´ S iff RI = SI
I ² R+ v R iff (RI)+ µ RI
• I satisfies a Tbox T (I ² T ) iff I satisfies every axiom A in T
• An interpretation I satisfies (models) an Abox axiom A (I ² A):
I ² x:D iff xI 2 DI I ² hx,yi:R iff (xI,yI) 2 RI
• I satisfies an Abox A (I ² A) iff I satisfies every axiom A in A
• I satisfies an KB K (I ² K) iff I satisfies both T and A

10. Short History of Description Logics
Phase 1:
• Incomplete systems (Back, Classic, Loom, . . . )
• Based on structural algorithms
Phase 2:
• Development of tableau algorithms and complexity 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

11. Recent Developments
Phase 4:
• Mainstream applications and tools
• Databases
• Consistency of conceptual schemata (EER, UML etc.)
• Schema integration
• Query subsumption (w.r.t. a conceptual schema)
• Ontologies, e-Science and Semantic Web/Grid
• Ontology engineering (schema design, maintenance, integration)
• Reasoning with ontology-based annotations (data)
• Mature implementations
• Research implementations
• FaCT, FaCT++, Racer, Pellet, …
• Commercial implementations
• Cerebra system from Network Inference (and now Racer)

12. Ontology: Origins and History
a philosophical discipline—a branch of philosophy that
deals with the nature and the organisation of reality
• Science of Being (Aristotle, Metaphysics, IV, 1)
• Tries to answer the questions:
• What characterizes being?
• Eventually, what is being?
• How should things be classified?

13. Ontology in Computer Science
• An ontology is an engineering artefact consisting of:
• A vocabulary used to describe (a particular view of)
some domain
• An explicit specification of the intended meaning of the
vocabulary.
• almost always includes how concepts should be classified
• Constraints capturing additional knowledge about the
domain
• Ideally, an ontology should:
• Capture a shared understanding of a domain of interest
• Provide a formal and machine manipulable model of
the domain

14. Example Ontology
• Vocabulary and meaning (“definitions”)
• Elephant is a concept whose members are a kind of animal
• Herbivore is a concept whose members are exactly those animals who eat
only plants or parts of plants
• Adult_Elephant is a concept whose members are exactly those elephants
whose age is greater than 20 years
• Background knowledge/constraints on the domain (“general axioms”)
• Adult_Elephants weigh at least 2,000 kg
• All Elephants are either African_Elephants or Indian_Elephants
• No individual can be both a Herbivore and a Carnivore

15. Where are ontologies used?
• e-Science, e.g., Bioinformatics
• The Gene Ontology
• The Protein Ontology (MGED)
• “in silico” investigations relating theory and data
• Medicine
• Terminologies
• Databases
• Integration
• Query answering
• User interfaces
• Linguistics
• The Semantic Web

16. Why Ontology Reasoning?
• Given key role of ontologies in many applications, it is essential to
provide tools and services to help users:
• Design and maintain high quality ontologies, e.g.:
• Meaningful — all named classes can have instances
• Correct — captured intuitions of domain experts
• Minimally redundant — no unintended synonyms
• Richly axiomatised — (sufficiently) detailed descriptions
• Answer queries over ontology classes and instances, e.g.:
• Find more general/specific classes
• Retrieve individuals/tuples matching a given query
• Integrate and align multiple ontologies

17. Why Decidable Reasoning?
• OWL is an W3C standard DL based ontology language
• OWL constructors/axioms restricted so reasoning is decidable
• Consistent with Semantic Web's layered architecture
• XML provides syntax transport layer
• RDF(S) provides basic relational language and simple ontological primitives
• OWL provides powerful but still decidable ontology language
• Further layers (e.g. SWRL) will extend OWL
• Will almost certainly be undecidable
• W3C requirement for “implementation experience”
• “Practical” decision procedures
• Several implemented systems
• Evidence of empirical tractability

18. Why Correct Reasoning?
• Need to have high level of confidence in reasoner
• Most interesting/useful inferences are those that were
unexpected
• Likely to be ignored/dismissed if reasoner known to be
unreliable
• Many realistic web applications will be agent ↔
agent
• No human intervention to spot glitches in reasoning

19. Use a (Description) Logic
• OWL DL based on SHIQ Description Logic
• In fact it is equivalent to SHOIN(Dn) DL
• OWL DL Benefits from many years of DL research
• Well defined semantics
• Formal properties well understood (complexity, decidability)
• Known reasoning algorithms
• Implemented systems (highly optimised)
• In fact there are three “species” of OWL (!)
• OWL full is union of OWL syntax and RDF
• OWL DL restricted to First Order fragment (¼ DAML+OIL)
• OWL Lite is “simpler” subset of OWL DL (equiv to SHIF(Dn))

20. Class/Concept Constructors
• C is a concept (class); P is a role (property); x is an individual name
• XMLS datatypes as well as classes in 8P.C and 9P.C
• Restricted form of DL concrete domains

21. RDFS Syntax
<owl:Class>
<owl:intersectionOf rdf:parseType=" collection">
<owl:Class rdf:about="#Person"/>
<owl:Restriction>
<owl:onProperty rdf:resource="#hasChild"/>
<owl:toClass>
<owl:unionOf rdf:parseType=" collection">
<owl:Class rdf:about="#Doctor"/>
<owl:Restriction>
<owl:onProperty rdf:resource="#hasChild"/>
<owl:hasClass rdf:resource="#Doctor"/>
</owl:Restriction>
</owl:unionOf>
</owl:toClass>
</owl:Restriction>
</owl:intersectionOf>
</owl:Class>
E.g., Person u 8hasChild.(Doctor t 9hasChild.Doctor):

22. Semantics
• Semantics defined by interpretations
• An interpretation I = (DI, ¢I), where
• DI 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 i ! iI element of DI

23. Semantics (cont.)
• Interpretation function ¢I extends to concept (and
role) expressions in the obvious way, i.e.:

24. Ontologies / Knowledge Bases
• OWL ontology equivalent to a DL Knowledge Base
• OWL ontology consists of a set of axioms and facts
• Note: an ontology is usually thought of as containing only
Tbox axioms (schema)---OWL is non-standard in this
respect
• Recall that a DL KB K is a pair hT ,Ai where
• T is a set of “terminological” axioms (the Tbox)
• A is a set of “assertional” axioms (the Abox)

25. Ontology/Tbox Axioms
• Obvious FO/Modal Logic equivalences
• E.g., DL: C v D FOL: x.C(x) !D(x) ML: C!D
• Often distinguish two kinds of Tbox axioms
• “Definitions” C v D or C ´ D where C is a concept name
• General Concept Inclusion axioms (GCIs) where C may be complex

26. Ontology Facts / Abox Axioms
• Note: using nominals (e.g., in SHOIN), can
reduce Abox axioms to concept inclusion axioms
• equivalent to
• equivalent to

27. Knowledge Base Semantics
• An interpretation I satisfies (models) an axiom A (I ² A):
• I ² C v D iff CI µ DI I ² C ´ D iff CI = DI
• I ² R v S iff RI µ SI I ² R ´ S iff RI = SI
• I ² x 2 D iff xI 2 DI
• I ² hx,yi 2 R iff (xI,yI) 2 RI
• I ² R+ v R iff (RI)+ µ RI
• I satisfies a Tbox T (I ² T ) iff I satisfies every axiom A in T
• I satisfies an Abox A (I ² A) iff I satisfies every axiom A in A
• I satisfies an KB K (I ² K) iff I satisfies both T and A

28. DL Reasoning: Basics
• Tableau algorithms used to test satisfiability (consistency)
• Try to build a tree-like model of the input concept C
• Decompose C syntactically
• Apply tableau expansion rules
• Infer constraints on elements of model
• Tableau rules correspond to constructors in logic (u, t etc)
• Some rules are nondeterministic (e.g., t, 6)
• In practice, this means search
• Stop when no more rules applicable or clash occurs
• Clash is an obvious contradiction, e.g., A(x), :A(x)
• Cycle check (blocking) may be needed for termination
• C satisfiable iff rules can be applied such that a fully expanded clash free
tree is constructed

29. DL Reasoning: Advanced Techniques
• Satisfiability w.r.t. an Ontology O
• For each axiom C v D 2 O , add :C t D to every node label
• More expressive DLs
• Basic technique can be extended to deal with
• Role inclusion axioms (role hierarchy)
• Number restrictions
• Inverse roles
• Concrete domains/datatypes
• Aboxes
• etc.
• Extend expansion rules and use more sophisticated blocking strategy
• Forest instead of Tree (for Aboxes)
• Root nodes correspond to individuals in Abox

30. Recent Developments
• Algorithms for NExpTime logics such as SHOIQ
• Increased expressive power (roles, keys, etc.)
• Graph based algorithms for Polynomial logics
• Automata based algorithms

31. Current Research
• Extending Description Logics
• Complex roles, finite domains, concrete domains, keys,
e-connections, …
• Future OWL extensions (e.g., with “rules”)
• Integrating with other logics/systems
• E.g., Answer Set Programming
• Alternative reasoning techniques
• Automata based algorithms
• Translation into datalog
• Graph based algorithms (for sub ALC languages)

32. Current Research
• Improving Scalability
• Very large ontologies
• Very large numbers of individuals
• Other reasoning tasks (non-standard inferences)
• Matching, LCS, explanation, querying, …
• Implementation of tools and Infrastructure
• More expressive languages (such as SHOIN)
• New algorithmic techniques
• Tools to support large scale ontological engineering
• Editing, visualisation, explanation, etc.

33. Summary
• DLs are a family of logic based Knowledge
Representation formalisms
• Describe domain in terms of concepts, roles and
individuals
• An Ontology is an engineering artefact consisting of:
• A vocabulary of terms
• An explicit specification their intended meaning
• Ontologies play a key role in many applications
• e-Science, Medicine, Databases, Semantic Web, etc.

34. Summary
• Reasoning is important
• Essential for design, maintenance and deployment of
ontologies
• Reasoning support based on DL systems
• Tableaux decision procedures
• Highly optimised implementations
• Many exciting challenges remain

35. Code example:
Ex2
# Defining properties and restrictions
ObjectProperty: hasChild
Class: Parent
SubClassOf: hasChild some Child
Class: Child
Ex1:
# Defining a concept hierarchy
Class: Person
SubClassOf: Animal
Class: Man
SubClassOf: Person
Class: Woman
SubClassOf: Person
Ex3
# Defining individuals and their properties
Individual: John
Types: Man
Individual: Mary
Types: Woman
Individual: Jane
Types: Child
Individual: John hasChild Jane