Combining Horn rules and description logics in CARIN

doi:10.1016/S0004-3702(98)00048-4

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Combining Horn rules and description logics in CARIN

Journal Article•DOI•

Combining Horn rules and description logics in CARIN

Alon Y. Levy¹, Marie-Christine Rousset²•Institutions (2)

University of Washington¹, University of Paris-Sud²

01 Sep 1998-Artificial Intelligence (Elsevier Science Publishers Ltd.)-Vol. 104, Iss: 1, pp 165-209

TL;DR: It is shown that in general, the reasoning problem for recursive carin - A LCNR knowledge bases is undecidable, and the constructors of ALCNR causing the undecidability is identified.

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About: This article is published in Artificial Intelligence.The article was published on 1998-09-01 and is currently open access. It has received 401 citations till now. The article focuses on the topics: Knowledge representation and reasoning & Description logic.

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Proceedings Article•DOI•

Data integration: a theoretical perspective

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Maurizio Lenzerini¹•Institutions (1)

Sapienza University of Rome¹

03 Jun 2002

TL;DR: The tutorial is focused on some of the theoretical issues that are relevant for data integration: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

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Abstract: Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

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2,716 citations

Cites background from "Combining Horn rules and descriptio..."

...Other papers consider the case of query containment in the presence of various types of constraints [5, 39, 32, 69, 71, 70, 20], and for regular-path queries and their extensions [47, 25, 28, 41]....
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Journal Article•DOI•

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

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Diego Calvanese¹, Giuseppe De Giacomo², Domenico Lembo², Maurizio Lenzerini², Riccardo Rosati² - Show less +1 more•Institutions (2)

Free University of Bozen-Bolzano¹, Sapienza University of Rome²

01 Oct 2007-Journal of Automated Reasoning

TL;DR: It is shown that, for the DLs of the DL-Lite family, the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is LogSpace in thesize of the ABox, which is the first result ofPolynomial-time data complexity for query answering over DL knowledge bases.

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Abstract: We propose a new family of description logics (DLs), called DL-Lite, specifically tailored to capture basic ontology languages, while keeping low complexity of reasoning. Reasoning here means not only computing subsumption between concepts and checking satisfiability of the whole knowledge base, but also answering complex queries (in particular, unions of conjunctive queries) over the instance level (ABox) of the DL knowledge base. We show that, for the DLs of the DL-Lite family, the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is LogSpace in the size of the ABox (i.e., in data complexity). To the best of our knowledge, this is the first result of polynomial-time data complexity for query answering over DL knowledge bases. Notably our logics allow for a separation between TBox and ABox reasoning during query evaluation: the part of the process requiring TBox reasoning is independent of the ABox, and the part of the process requiring access to the ABox can be carried out by an SQL engine, thus taking advantage of the query optimization strategies provided by current database management systems. Since even slight extensions to the logics of the DL-Lite family make query answering at least NLogSpace in data complexity, thus ruling out the possibility of using on-the-shelf relational technology for query processing, we can conclude that the logics of the DL-Lite family are the maximal DLs supporting efficient query answering over large amounts of instances.

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1,482 citations

Cites methods from "Combining Horn rules and descriptio..."

...Alternative reasoning procedures that allow for clearly isolating data complexity have recently been proposed, but how they will work in practice still needs to be understood: in [24], a coNP upper bound for data complexity of instance checking in an expressive DL has been shown, and a polynomial fragment has been isolated, though it is open whether the technique can be extended to deal efficiently with conjunctive queries; building on the technique proposed in [26], coNP-completeness of answering conjunctive queries for an expressive DL with assertions, inverse roles, and number restrictions (that generalize functionality) has been shown in [27]....
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Proceedings Article•DOI•

Data integration: the teenage years

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Alon Halevy¹, Anand Rajaraman, Joann J. Ordille²•Institutions (2)

Google¹, Avaya²

01 Sep 2006

650 citations

Cites methods from "Combining Horn rules and descriptio..."

...[70] Alon Levy and Marie-Christine Rousset....
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...In fact, the idea of LAV was inspired by the fact that data sources need to be represented declaratively, and the mediated schema of the Information Manifold was based on Classic Description Logic [17] and on work combining the expressive power of Description Logics with database query languages [10, 70]....
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Journal Article•DOI•

Query Answering for OWL-DL with rules

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Boris Motik¹, Ulrike Sattler², Rudi Studer¹•Institutions (2)

Karlsruhe Institute of Technology¹, University of Manchester²

01 Jul 2005-Journal of Web Semantics

TL;DR: A decidable combination of OWL-DL and function-free Horn rules where rules are required to be DL-safe: each variable in the rule is required to occur in a non-DL-atom in therule body.

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598 citations

Cites background from "Combining Horn rules and descriptio..."

...A comprehensive study of the effects of combining datalog rules with description logics is presented in [16]....
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...Unsurprisingly, this and similar combinations are undecidable [16, 11]....
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...Investigating this proof and the ones in [16] more closely, we note that the undecidability is caused by the interaction between some very basic features of description logics and rules....
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...Generalizing the approaches of other decidable combinations of rules and description logics [16, 5], in DL-safe rules, concepts and roles are allowed to occur in both rule bodies and heads as unary, respectively binary predicates in atoms, but each variable of a rule is required to occur in some body literal whose predicate is neither a concept nor a role....
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Journal Article•DOI•

The DL-lite family and relations

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Alessandro Artale¹, Diego Calvanese¹, Roman Kontchakov², Michael Zakharyaschev²•Institutions (2)

Free University of Bozen-Bolzano¹, Birkbeck, University of London²

01 Sep 2009-Journal of Artificial Intelligence Research

TL;DR: This article carries out a thorough and systematic investigation of inference in extensions of the original DL-Lite logics along five axes, by adding the Boolean connectives and number restrictions to concept constructs and adopting or dropping the unique name assumption.

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Abstract: The recently introduced series of description logics under the common moniker 'DL-Lite' has attracted attention of the description logic and semantic web communities due to the low computational complexity of inference, on the one hand, and the ability to represent conceptual modeling formalisms, on the other. The main aim of this article is to carry out a thorough and systematic investigation of inference in extensions of the original DL-Lite logics along five axes: by (i) adding the Boolean connectives and (ii) number restrictions to concept constructs, (iii) allowing role hierarchies, (iv) allowing role disjointness, symmetry, asymmetry, reflexivity, irreflexivity and transitivity constraints, and (v) adopting or dropping the unique name assumption. We analyze the combined complexity of satisfiability for the resulting logics, as well as the data complexity of instance checking and answering positive existential queries. Our approach is based on embedding DL-Lite logics in suitable fragments of the one-variable first-order logic, which provides useful insights into their properties and, in particular, computational behavior.

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592 citations

Additional excerpts

...…2003; Berardi, Calvanese, & De Giacomo, 2005; Artale et al., 1996, 2007, 2007b), • information and data integration (Beeri, Levy, & Rousset, 1997; Levy & Rousset, 1998; Goasdoue, Lattes, & Rousset, 2000; Calvanese et al., 1998a, 2002a, 2002b, 2008; Noy, 2004; Meyer, Lee, & Booth, 2005), •…...
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...information and data integration (Beeri et al., 1997; Levy & Rousset, 1998; Goasdoue et al., 2000; Calvanese et al., 1998a, 2002a, 2002b, 2008; Noy, 2004; Meyer et al., 2005),...
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References

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P. Brezillon, P. Bouquet

01 Jan 1999

TL;DR: The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.

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Abstract: LNAI was established in the mid-1980s as a topical subseries of LNCS focusing on artificial intelligence. This subseries is devoted to the publication of state-of-the-art research results in artificial intelligence, at a high level and in both printed and electronic versions making use of the well-established LNCS publication machinery. As with the LNCS mother series, proceedings and postproceedings are at the core of LNAI; however, all other sublines are available for LNAI as well. The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.

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3,464 citations

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Principles of database and knowledge-base systems

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Jeffrey D. Ullman

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TL;DR: This book goes into the details of database conception and use, it tells you everything on relational databases from theory to the actual used algorithms.

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Abstract: This book goes into the details of database conception and use, it tells you everything on relational databases. from theory to the actual used algorithms.

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2,475 citations

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Attributive concept descriptions with complements

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Manfred Schmidt-Schaubß¹, Gert Smolka²•Institutions (2)

Software AG¹, Saarland University²

01 Feb 1991-Artificial Intelligence

TL;DR: It is shown that deciding coherence and subsumption of such descriptions are PSPACE-complete problems that can be decided with linear space.

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1,105 citations

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Semistructured data

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Peter Buneman¹•Institutions (1)

University of Pennsylvania¹

01 May 1997

TL;DR: A number of issues surrounding semistructured data are covered: finding a concise formulation, building a sufficiently expressive language for querying and transformation, and optimizat,ion problems.

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Abstract: In semistructured data, the information that is normally associated with a schema is contained within the data, which is sometimes called “self-describing”. In some forms of semistructured data there is no separate schema, in others it exists but only places loose constraints on the data. Semistructured data has recently emerged as an important topic of study for a variety of reasons. First, there are data sources such as the Web, which we would like to treat as databases but which cannot be constrained by a schema. Second, it may be desirable to have an extremely flexible format for data exchange between disparate databases. Third, even when dealing with structured data, it may be helpful to view it. as semistructured for the purposes of browsing. This tutorial will cover a number of issues surrounding such data: finding a concise formulation, building a sufficiently expressive language for querying and transformation, and optimizat,ion problems.

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Foundations of deductive databases and logic programming

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Jack Minker¹•Institutions (1)

University of Maryland, College Park¹

01 Aug 1988

TL;DR: This book discusses Negation in Logic Programming, a Theory of Declarative Knowledge, and its Applications in Deductive Databases and Implementation, as well as other topics.

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Abstract: Introduction, by J. Minker Part I - Negation and Stratified Databases Chapter 1 Negation in Logic Programming, by J.C. Shepherdson Chapter 2 Towards a Theory of Declarative Knowledge, by K.R. Apt, H.A. Blair, and A. Walker Chapter 3 Negation as Failure Using Tight Derivations for General Logic Programs, by A. Van Gelder Chapter 4 On the Declarative Semmantics of Logic Programs with Negation, by V. Lifschitz Chapter 5 On the Declarative Semantics of Deductive Databases and Logic Programs, by T.C. Przymusinski Chapter 6 On Domain Independent Databases, by R.W. Topor and E.A. Sonenberg Part II - Fundamental Issues in Deductive Databases and Implementation Chapter 7 Foundations of Semantic Query Optimization for Deductive Databases, by U.S. Chakravarthy, J. Grant, and J. Minker Chapter 8 Intelligent Query Answering in Rule Based Systems, by T. Imielinski Chapter 9 A Theorem-Proving Approach to Database Integrity, by F. Sadri and R. Kowalski Chapter 10 A Logic-based Language for Database Updates, by S. Manchanda and D.S. Warren Chapter 11 Compiling the GCWA in Indefinite Deductive Databases, by L. Henschen and H. Park Chapter 12 Performance Evaluation of Data Intensive Logic Programs, by F. Bancilhon and R. Ramakrishnan Chapter 13 A Superjoin Algorithm for Deductive Databases, by J.A. Thom, K. Ramamohanarao, and L. Naish Part III - Unification and Logic Programs Chapter 14 Logic Programming and Parallel Complexity, by P.C. Kanellakis Chapter 15 Unification Revisited, by J-L Lassez, M.J. Maher, and K. Marriott Chapter 16 Equivalences of Logic Programs, by M.J. Maher Chapter 17 Optimizing Datalog Programs, by Y. Sagiv Chapter 18 Converting AND-Control to OR-Control by Program Transformation, by M.H. van Emden and P. Szeredi Authors Referees Author Index Subject Index

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707 citations