Description logics (DL) are a family of formal knowledge representation languages. They are more expressive than propositional logic but have more efficient decision problems than firstorder predicate logic.
DLs are used in Artificial Intelligence for formal reasoning on the concepts of an application domain (known as terminological knowledge). They are of particular importance in providing a logical formalism for Ontologies and the Semantic Web. The most notable application outside information science is in bioinformatics where DLs assist in the codification of medical knowledge.
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A Description Logic (DL) models concepts, roles and individuals, and their relationships.
The fundamental modeling concept of a DL is the axiom  a logical statement relating roles and/or concepts^{[1]}. This is a key difference from the frames paradigm where a frame specification declares and completely defines a class^{[2]}.
OWL  DL 

class  concept 
property  role 
individual  object 
There are many varieties of Description Logics and there is an informal naming convention, roughly describing the operators allowed. The expressivity is encoded in the label for a logic using the following letters:
Functional properties.  
Full existential qualification (Existential restrictions that
have fillers other than owl:thing ). 

Concept union.  
Complex concept negation.  
An abbreviation for with transitive roles.  
Role hierarchy (subproperties  rdfs:subPropertyOf).  
Limited complex role inclusion axioms; reflexivity and irreflexivity; role disjointness.  
Nominals. (Enumerated classes of object value restrictions 
owl:oneOf , owl:hasValue ). 

Inverse properties.  
Cardinality restrictions (owl:Cardinality ,
owl:MaxCardinality ). 

Qualified cardinality restrictions (available in OWL 2,
cardinality restrictions that have fillers other than
owl:thing ). 

Use of datatype properties, data values or data types. 
As an example, is a centrally important description logic from which comparisons with other varieties can be made. is simply with complement of any concept allowed, not just atomic concepts.
A further example, the description logic is the logic plus extended cardinality restrictions, and transitive and inverse roles. The naming conventions aren't purely systematic so that the logic might be referred to as and abbreviations are made where possible, is used instead of the equivalent .
The Protégé ontology editor supports . Three major bioinformatic terminology bases, Snomed, Galen, and GO, are expressible in (with additional role properties).
OWL 2 provides the expressiveness of , OWLDL is based on , and for OWLLite it is .
Some canonical DLs that do not exactly fit this convention are:
Attributive language. This is the base language which allows:  


A sublanguage of , which is obtained by disallowing limited existential quantification.  
Intersection and full existential restriction. 
Description logic (DL) was given its current name in the 1980s. Previous to this it was called (chronologically): terminological systems, and concept languages.
Frames and semantic networks lack formal (logicbased) semantics^{[3]}. DLs were first introduced into Knowledge Representation (KR) systems to overcome this deficiency^{[4]}.
The first DLbased KR system was KLONE (by Brachman and Schmolze, 1985). During the 80's other DLbased systems using structural subsumption algorithms^{[5]} were developed including KRYPTON (1983), LOOM (1987), BACK (1988), KREP (1991) and CLASSIC (1991). This approach featured DLs with limited expressiveness but relatively efficient (polynomial time) reasoning ^{[6]}.
In the early 90's, the introduction of a new tableau based algorithm paradigm allowed efficient reasoning on more expressive DLs^{[7]}. DLbased systems using these algorithms  such as KRIS (1991)  show acceptable reasoning performance on typical inference problems even though the worst case complexity is no longer polynomial^{[8]}. From the mid 90's, reasoners were created with good practical performance on very expressive DLs with high worst case complexity^{[9]}. Examples from this period include FaCT (1998), RACER (2001), CEL (2005), and KAON 2 (2005).
The DARPA Agent Markup Language (DAML) and Ontology Inference Layer (OIL) ontology languages for the semantic web can be viewed as syntactic variants of DLs^{[10]}. In particular, the formal semantics and reasoning in OIL use the DL ^{[11]}. The DAML+OIL DL was developed as a submission to^{[12]}  and formed the starting point of  the World Wide Web Consortium (W3C) Web Ontology Working Group^{[13]}. In 2004, the Web Ontology Working Group completed its work by issuing the OWL^{[14]} recommendation. The design of OWL is based on the family of DLs^{[15]} with OWL DL and OWL Lite based on and respectively^{[16]}.
The W3C OWL Working Group began work in 2007 on a refinement of  and extension to  OWL^{[17]}. In 2009, this was completed by the issuance of the OWL2 recommendation^{[18]}. Practical experience demonstrated that OWL DL lacked several key features necessary to model complex domains^{[19]}.
In DLs, a distinction is drawn between the socalled TBox (terminological box) and the ABox (assertional box). In general, the TBox contains sentences describing concept hierarchies (i.e., relations between concepts) while the ABox contains ground sentences stating where in the hierarchy individuals belong (i.e., relations between individuals and concepts). For example, the statement:
(1) Every employee is a person
belongs in the TBox, while the statement:
(2) Bob is an employee
belongs in the ABox.
Note that the TBox/ABox distinction is not significant, in the same sense that the two "kinds" of sentences are not treated differently in firstorder logic (which subsumes most DLs). When translated into firstorder logic, a subsumption axiom like (1) is simply a conditional restriction to unary predicates (concepts) with only variables appearing in it. Clearly, a sentence of this form is not privileged or special over sentences in which only constants ("grounded" values) appear like (2).
So why was the distinction introduced? The primary reason is that the separation can be useful when describing and formulating decisionprocedures for various DLs. For example, a reasoner might process the TBox and ABox separately, in part because certain key inference problems are tied to one but not the other one ('classification' is related to the TBox, 'instance checking' to the ABox). Another example is that the complexity of the TBox can greatly affect the performance of a given decisionprocedure for a certain DL, independently of the ABox. Thus, it is useful to have a way to talk about that specific part of the knowledge base.
The secondary reason is that the distinction can make sense from the knowledge base modeler's perspective. It is plausible to distinguish between our conception of terms/concepts in the world (class axioms in the TBox) and particular manifestations of those terms/concepts (instance assertions in the ABox). In the above example: when the hierarchy within a company is the same in every branch but the assignment to employees is different in every department (because there are other people working there), this distinction makes sense to reuse the TBox for different branches.
There are two features of Description Logics that are not shared by most other data description formalisms: DLs do not make the Unique Name Assumption (UNA) or the Closed World Assumption (CWA). Not having UNA means that two concepts with different names may be allowed by some inference to be shown to be equivalent. Not having CWA, or rather having the Open World Assumption (OWA) means that lack of knowledge of a fact does not immediately imply knowledge of the negation of a fact.
Like first order logic (FOL), a syntax defines which collections of symbols are legal expressions in a Description Logic (DL), and semantics determine meaning. Unlike FOL, a DL may have several well known syntactic variants ^{[20]}.
The syntax of a member of the description logic family is characterized by its recursive definition, in which the constructors that can be used to form concept terms are stated. Some constructors are related to logical constructors in firstorder logic (FOL) such as intersection or conjunction of concepts, union or disjunction of concepts, negation or complement of concepts, universal restriction and existential restriction. Other constructors have no corresponding construction in FOL including restrictions on roles for example, inverse, transitivity and functionality.
Let C and D be concepts, a and b be individuals, and R be a role.
Symbol  Description  Example  Read 

all concept names  top  
empty concept  bottom  
intersection or conjunction of concepts  C and D  
union or disjunction of concepts  C or D  
negation or complement of concepts  not C  
universal restriction  all Rsuccessors are C  
existential restriction  an Rsuccessor exists in C  
Concept inclusion  all C are D  
Concept equivalence  C is equivalent to D  
Concept definition  C is defined to be equal to D  
:  Concept assertion  a:C  a is a C 
:  Role assertion  (a,b):R  a is Rrelated to b 
The prototypical DL Attributive Concept Language with Complements () was introduced by Manfred SchmidtSchauß and Gert Smolka in 1991, and is the basis of many more expressive DLs^{[21]}. The following definitions follow the treatment in Baader et al^{[22]}.
Let N_{C}, N_{R} and N_{O} be (respectively) sets of concept names (also known as an atomic concepts), role names and individual names (also known as individuals or objects). Then the ordered triple (N_{C}, N_{R}, N_{O} ) is the signature.
The set of concepts is the smallest set such that:
A general concept inclusion (GCI) has the form where C and D are concepts. Write when and . A TBox is any finite set of GCIs.
An ABox is a finite set of assertional axioms.
A knowledge base (KB) is an ordered pair for TBox and ABox .
The semantics of description logics is defined by interpreting concepts as sets of individuals and roles as sets of pairs of individuals. Those individuals are typically assumed from a given domain. The semantics of non atomic concepts and roles is then defined in terms of atomic concepts and roles. This is done by using a recursive definition similar to the syntax.
The following definitions follow the treatment in Baader et al^{[23]}.
A terminological interpretation over a signature (N_{C},N_{ R},N_{O}) consists of
such that
Define (read I models) as follows
Let be a knowledge base.
In addition to the ability to describe concepts formally, one also would like to employ the description of a set of concepts to ask questions about the concepts and instances described. The most common decision problems are basic databasequerylike questions like instance checking (is a particular instance (member of an Abox) a member of a given concept) and relation checking (does a relation/role hold between two instances, in other words does a have property b), and the more globaldatabasequestions like subsumption (is a concept a subset of another concept), and concept consistency (is there no contradiction among the definitions or chain of definitions). The more operators one includes in a logic and the more complicated the Tbox (having cycles, allowing nonatomic concepts to include each other), usually the higher the computational complexity is for each of these problems (see Navigator on Description Logic Complexity for examples).
Many Description Logics (DLs) are decidable fragments of first order logic (FOL)^{[24]}. Some DLs now include operations (for example, transitive closure of roles) that allow efficient inference but cannot be expressed in FOL^{[25]}.
Fuzzy description logic combines fuzzy logic with DLs. Since many concepts that are needed for intelligent systems lack well defined boundaries, or precisely defined criteria of membership, we need fuzzy logic to deal with notions of vagueness and imprecision. This offers a motivation for a generalization of description logics towards dealing with imprecise and vague concepts.
What people should also think about for intelligent systems is multiple viewpoints of the data. This will lead to subjective (as opposed to objective) intelligent systems.
Description Logics (DLs) are related to  but developed independently of  Modal Logic (ML)^{[26]}. Many  but not all  DLs are syntactic variants of MLs^{[27]}.
DL  ML 

K^{[28]} 
Temporal Description Logics represent  and allow reasoning about  time dependent concepts and many different approaches to this problem exist^{[29]}. For example, a description logic might be combined with a modal temporal logic such as Linear temporal logic.
There are some reasoners to deal with OWL and Description Logics. These are some of the most popular:
DL reasoners, such as FaCT, FaCT++, RACER, DLP and Pellet, implement the analytic tableau method. KAON2 is implemented by algorithms which reduce a SHIQ(D) knowledge base to a disjunctive datalog program.
