Reasoning in Description Logics by a Reduction to Disjunctive Datalog

Hustadt, Ullrich; Motik, Boris; Sattler, Ulrike

doi:10.1007/s10817-007-9080-3

Cited by 171 publications

(214 citation statements)

References 31 publications

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“…These include resolution based approaches [102,104], automata based approaches [49,162], and structural approaches (for sub-Boolean DLs) [6]. The most widely used technique, however, is the tableau based approach first introduced by SchmidtSchauß and Smolka [150].…”

Section: Tableau Based Reasoning Techniquesmentioning

confidence: 99%

“…TBoxes can be decided within exponential time [49,117,116,160], 6 whereas this is very hard to prove using a tableau based approach [66]. Resolution based approaches [103,5,104,107], which use the translation of DLs into first-order predicate logic, may have the advantage that they simultaneously yield a decision procedure for a certain decidable DL, and a semidecision procedure for a more expressive logic (such as OWL Full or first-order predicate logic). Moreover, some of them are worst-case optimal [104], and others can be implemented through appropriate parameterization of existing first-order theorem provers [103].…”

Section: Other Reasoning Techniquesmentioning

confidence: 99%

“…Resolution based approaches [103,5,104,107], which use the translation of DLs into first-order predicate logic, may have the advantage that they simultaneously yield a decision procedure for a certain decidable DL, and a semidecision procedure for a more expressive logic (such as OWL Full or first-order predicate logic). Moreover, some of them are worst-case optimal [104], and others can be implemented through appropriate parameterization of existing first-order theorem provers [103]. Finally, the translation of very expressive DLs into propositional dynamic logic or the propositional mu-calculus [57,58,56,59] allows one to transfer known decidability and complexity results for these logics to very expressive DLs.…”

Section: Other Reasoning Techniquesmentioning

confidence: 99%

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Chapter 3 Description Logics

Baader¹,

Horrocks²,

Sattler³

2008

Foundations of Artificial Intelligence

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152

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In this chapter we will introduce description logics, a family of logic-based knowledge representation languages that can be used to represent the terminological knowledge of an application domain in a structured way. We will first review their provenance and history, and show how the field has developed. We will then introduce the basic description logic ALC in some detail, including definitions of syntax, semantics and basic reasoning services, and describe important extensions such as inverse roles, number restrictions, and concrete domains. Next, we will discuss the relationship between description logics and other formalisms, in particular first order and modal logics; the most commonly used reasoning techniques, in particular tableau, resolution and automata based techniques; and the computational complexity of basic reasoning problems. After reviewing some of the most prominent applications of description logics, in particular ontology language applications, we will conclude with an overview of other aspects of description logic research, and with pointers to the relevant literature. IntroductionDescription logics (DLs) [14,25,50] are a family of knowledge representation languages that can be used to represent the knowledge of an application domain in a structured and formally well-understood way. The name description logics is motivated by the fact that, on the one hand, the important notions of the domain are described by concept descriptions, i.e., expressions that are built from atomic concepts (unary predicates) and atomic roles (binary predicates) using the concept and role constructors provided by the particular DL; on the other hand, DLs differ from their predecessors, such as semantic networks and frames, in that they are equipped with a formal, logic-based semantics.We will first illustrate some typical constructors by an example; formal definitions will be given in Section 3.2. Assume that we want to define the concept of "A man that is married to a doctor, and all of whose children are either doctors or professors." This concept can be described with the following concept description:Human ¬Female (∃married.Doctor) (∀hasChild.(Doctor Professor)).

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Section: Tableau Based Reasoning Techniquesmentioning

confidence: 99%

Section: Other Reasoning Techniquesmentioning

confidence: 99%

Section: Other Reasoning Techniquesmentioning

confidence: 99%

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Chapter 3 Description Logics

Baader¹,

Horrocks²,

Sattler³

2008

Foundations of Artificial Intelligence

Self Cite

152

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show abstract

“…Transformation to a formalism with lower complexity comes inevitably at some cost, which usually means an exponential increase in the size of the knowledge base. Popular examples of this in Description Logics are first-order rewriting of conjunctive query answering over DL-Lite ontologies [8] or the reduction of SHIQ to disjunctive datalog [30]. 4.…”

Section: Introductionmentioning

confidence: 99%

Uniform Evaluation of Nonmonotonic DL-Programs

Eiter

Krennwallner

Schneider

et al. 2012

Lecture Notes in Computer Science

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“…Other reasoners scale to large Aboxes in secondary storage (e.g. Kaon2 [9], SHER [10]). The combination of a large Abox and a large Tbox required for this case study, however, far exceeds the size of the knowledge bases that have been tested so far with these reasoners.…”

Section: Introductionmentioning

confidence: 99%

Matching Patient Records to Clinical Trials Using Ontologies

et al. 2007

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Abstract. This paper describes a large case study that explores the applicability of ontology reasoning to problems in the medical domain. We investigate whether it is possible to use such reasoning to automate common clinical tasks that are currently labor intensive and error prone, and focus our case study on improving cohort selection for clinical trials. An obstacle to automating such clinical tasks is the need to bridge the semantic gulf between raw patient data, such as laboratory tests or specific medications, and the way a clinician interprets this data. Our key insight is that matching patients to clinical trials can be formulated as a problem of semantic retrieval. We describe the technical challenges to building a realistic case study, which include problems related to scalability, the integration of large ontologies, and dealing with noisy, inconsistent data. Our solution is based on the SNOMED CT R ontology, and scales to one year of patient records (approx. 240,000 patients).

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Reasoning in Description Logics by a Reduction to Disjunctive Datalog

Cited by 171 publications

References 31 publications

Chapter 3 Description Logics

Chapter 3 Description Logics

Uniform Evaluation of Nonmonotonic DL-Programs

Matching Patient Records to Clinical Trials Using Ontologies

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