The Description Logic Handbook: Theory, Implementation, and Applications

By F. Baader, Deborah L. McGuinness, Daniele Nardi, Peter F. Patel-Schneider. Description Logic Handbook, 2003

6,922 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/713c531396dc37f70543b1664de7e73e0633609c

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Citations in the corpus

The MASTRO system for ontology-based data access (2011, background): “a given TBox are concept satisfiability and concept subsumption [2].”
Fuzzy Ontology Representation using OWL 2 (2010, unknown): “Key words: Fuzzy OWL 2, Fuzzy Ontologies, Fuzzy Languages for the Semantic Web, Fuzzy Description Logics”
Description Logic Knowledge and Action Bases (2014, background): “Description Logics (DLs) (Baader et al., 2003) are knowledge representation formalisms that are tailored for representing the domain of interest in terms of concepts (or classes), denoting sets of objects, and roles (or relations), denoting binary relations between objects.”
Beyond Concepts: Ontology as Reality Representation (2004, background): “[11] This means that in DL circles talk of concepts is talk of certain syntactic entities .”
Reasoning on UML class diagrams (2005, methodology): “Notably ALCQI includes inverse roles P − , which allow for talking about the inverse of a relation, and qualified number restrictions, which are the most general form of cardinality constraints on roles.”
Handbook of satisfiability (2009, methodology): “We refer the reader to the bibliography presented above and to [5] for a detailed description of those topics.”
Explaining Trained Neural Networks with Semantic Web Technologies: First Steps (2017, background): “Description logics [1,12] are a major paradigm in knowledge representation as a subﬁeld of artiﬁcial intelligence.”
Automatic Composition of E-services That Export Their Behavior (2003, methodology): “From a practical point of view, because of the correspondence between Propositional Dynamic Logics (which DPDL belongs to) and Description Logics (DLs [8]), one can use current highly optimized DL-based systems [3]7 to check the existence of e-Service compositions.”
Automated analysis of feature models 20 years later: A literature review (2010, background): “Description logicsare a family of knowledge representation languages enabling the reasoning within knowledge domains by using specific logic reasoners [3].”
FaCT++ Description Logic Reasoner: System Description (2006, methodology): “Description Logics (DLs) are a family of logic based knowledge representation formalisms [1].”
Ontologies and the semantic web (2006, background): “A key feature of OWL is its basis in Description Logics (DLs), a family of logic-based knowledge representation formalisms that are descendants of Semantic Networks and KL-ONE, but that have a formal semantics based on firstorder logic [1].”
Automated Planning and Acting (2016, methodology): “Other approaches use Description Logic (DL), a fragment of first-order logic, to handle statements about objects, properties, relations, and their instances with inference algorithms for querying large stores of data and models over the Web [27].”
Applying Fuzzy DLs in the Extraction of Image Semantics (2009, methodology): “Aiming to enhance the utilisation of semantics and alleviate part of the aforementioned effects in the accuracy and completeness of descriptions that are extracted by means of machine learning approaches, we present in this paper a reasoning framework that utilises fuzzy DLs semantics in order to in”
OWL rules: A proposal and prototype implementation (2005, methodology): “The process of selecting information relevant to a concept expression E looks very much the same as unfolding (see [1]), and assumes that the KB is separated into a set of unfoldable axioms and a set of GCIs [25] and rules.”