Tractable Reasoning and Efficient Query Answering in Description Logics: The DL-Lite Family

By Diego Calvanese, Giuseppe De Giacomo, Domenico Lembo, Maurizio Lenzerini, Riccardo Rosati. Journal of automated reasoning, 2007

1,591 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/0d07b93cb2ee53cdfa27b116102f38c8e8b921f7

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Citations in the corpus

Ontology-Based Data Access: A Survey (2018, methodology): “It is based on the DL-Lite family [ Calvanese et al. , 2007; Artale et al. , 2009 ] , a suite of DLs closely related to conceptual modelling formalisms for DB design and software engineering.”
The DL-Lite Family and Relations (2009, methodology): “We begin by defining the logic DL-LiteHNbool , which can be regarded as the supremum of the original DL-Lite family (Calvanese et al., 2005, 2006, 2007b) in the lattice of description logics.”
Deep learning for noise-tolerant RDFS reasoning (2019, background): “In [86], the authors extend the clash queries [87] for DL-Lite [88] and caching to reduce the required calls to a semantic reasoner in order to check ABox inconsistency.”
OWL 2: The next step for OWL (2008, methodology): “The OWL 2 QL Profile OWL 2 QL is based on the DL-Lite family of description logics [7], which has been designed to allow for efficient reasoning with large amounts of data structured according to relatively simple schemata.”
ELP: Tractable Rules for OWL 2 (2008, background): “DL-based formalisms that fall into that category are EL [2], DL Lite [3], and DLP [4].”
Description Logic Knowledge and Action Bases (2014, methodology): “Notice that this functional view is tightly related to an epistemic interpretation of the KB (Calvanese, De Giacomo, Lembo, Lenzerini, & Rosati, 2007a).”
Data Complexity of Query Answering in Description Logics (2006, positive): “However, differently from [18], both the reduce and the atom rewrite steps have now to deal with the presence of n-ary relations, both in the query and in the inclusions, and the atom rewrite step has to properly manage the presence of conjunctions in concept PIs.”
The MASTRO system for ontology-based data access (2011, methodology): “For the logics of the DL-Lite family it has been shown that for unions of conjunctive queries (UCQs), under the unique name assumption, query answering can be carried out efficiently in the size of the data, by reducing it to SQL query evaluation over the ABox seen as a database [5].”
A general datalog-based framework for tractable query answering over ontologies (2009, background): “Georg Gottlob also gratefully acknowledges a Royal Society Wolfson Research Merit Award.”
Linking Data to Ontologies (2008, background): “From the results of [8,6,27], we have the following:”
Ontop: Answering SPARQL queries over relational databases (2016, methodology): “OWL 2 QL is based on the DL-Lite family of lightweight description logics [11,3], which guarantees that queries over the ontology can be rewritten into equivalent queries over the data alone.”
Conjunctive Query Answering for the Description Logic SHIQ (2007, methodology): “Very recently, an automata-based decision procedure for positive existential path queries over ALCQIbreg knowledge bases has been presented ( Calvanese, Eiter, & Ortiz, 2007 ).”
On rules with existential variables: Walking the decidability line (2011, background): “For instance, [13] shows that the major members of the DL-Lite family [18] are covered by guarded rules (plus specific equality rules and negative constraints, which do not interfere with query answering, see Section 9).”