Description logics (DL) are a family of formal knowledge representation languages.
There are general, spatial, temporal, spatiotemporal, and fuzzy description logics, and each description logic features a different balance between expressive power and reasoning complexity by supporting different sets of mathematical constructors.
[1] DLs are used in artificial intelligence to describe and reason about the relevant concepts of an application domain (known as terminological knowledge).
[citation needed] A description logic (DL) models concepts, roles and individuals, and their relationships.
There are many varieties of description logics and there is an informal naming convention, roughly describing the operators allowed.
Three major biomedical informatics terminology bases, SNOMED CT, GALEN, and GO, are expressible in
[5] DL was first introduced into knowledge representation (KR) systems to overcome this deficiency.
During the '80s other DL-based systems using structural subsumption algorithms[5] were developed including KRYPTON (1983), LOOM (1987), BACK (1988), K-REP (1991) and CLASSIC (1991).
This approach featured DL with limited expressiveness but relatively efficient (polynomial time) reasoning.
[5] In the early '90s, the introduction of a new tableau based algorithm paradigm allowed efficient reasoning on more expressive DL.
[5] DL-based 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.
[5] From the mid '90s, reasoners were created with good practical performance on very expressive DL with high worst case complexity.
DL reasoners, such as FaCT, FaCT++,[6] RACER, DLP and Pellet,[7] implement the method of analytic tableaux.
KAON2 is implemented by algorithms which reduce a SHIQ(D) knowledge base to a disjunctive datalog program.
The DARPA Agent Markup Language (DAML) and Ontology Inference Layer (OIL) ontology languages for the Semantic Web can be viewed as syntactic variants of DL.
[16] Practical experience demonstrated that OWL DL lacked several key features necessary to model complex domains.
Note that the TBox/ABox distinction is not significant, in the same sense that the two "kinds" of sentences are not treated differently in first-order logic (which subsumes most DL).
When translated into first-order logic, a subsumption axiom like (1) is simply a conditional restriction to unary predicates (concepts) with only variables appearing in it.
Another example is that the complexity of the TBox can greatly affect the performance of a given decision-procedure for a certain DL, independently of the ABox.
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), it makes sense to reuse the TBox for different branches that do not use the same ABox.
Not having UNA means that two concepts with different names may be allowed by some inference to be shown to be equivalent.
[8] 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.
Other constructors have no corresponding construction in FOL including restrictions on roles for example, inverse, transitivity and functionality.
) was introduced by Manfred Schmidt-Schauß and Gert Smolka in 1991, and is the basis of many more expressive DLs.
The more operators one includes in a logic and the more complicated the TBox (having cycles, allowing non-atomic concepts to include each other), usually the higher the computational complexity is for each of these problems (see Description Logic Complexity Navigator for examples).
Since many concepts that are needed for intelligent systems lack well defined boundaries, or precisely defined criteria of membership, fuzzy logic is needed to deal with notions of vagueness and imprecision.
This offers a motivation for a generalization of description logic towards dealing with imprecise and vague concepts.
[17] Temporal description logic represents—and allows reasoning about—time dependent concepts and many different approaches to this problem exist.