A general Datalog-based framework for tractable query answering over ontologies

Calì, Andrea; Gottlob, Georg; Lukasiewicz, Thomas

doi:10.1016/j.websem.2012.03.001

Cited by 305 publications

(315 citation statements)

References 69 publications

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“…This paradigm has attracted much interest recently, and has been studied under a variety of names such as Datalog+/-, ∀∃-rules, and -primarily in the database community -tuple-generating dependencies (TGDs) [2,3,6,7,8,9,13,14]. As in the combination of rules and DL, reasoning with existential rules is undecidable without further restrictions.…”

Section: Existential Rulesmentioning

confidence: 99%

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A better uncle for OWL

Krötzsch

Maier

Krisnadhi

et al. 2011

Proceedings of the 20th International Conference on World Wide Web

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We propose a description-logic style extension of OWL 2 with nominal schemas which can be used like "variable nominal classes" within axioms. This feature allows ontology languages to express arbitrary DL-safe rules (as expressible in SWRL or RIF) in their native syntax. We show that adding nominal schemas to OWL 2 does not increase the worst-case reasoning complexity, and we identify a novel tractable language SROELV3(⊓, ×) that is versatile enough to capture the lightweight languages OWL EL and OWL RL.

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Section: Existential Rulesmentioning

confidence: 99%

“…A chief interest of many of the above works thus is to establish formalisms for which (conjunctive) query answering is decidable, possibly with a low data complexity. For example, it has been shown that certain dialects of Datalog+/-capture and extend languages of the DL-Lite family [7,10].…”

Section: Existential Rulesmentioning

confidence: 99%

A better uncle for OWL

Krötzsch

Maier

Krisnadhi

et al. 2011

Proceedings of the 20th International Conference on World Wide Web

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“…The standard reasoning formalism for RDF data is OWL, with its flavors OWL Full, OWL DL and OWL lite. In [9], it is shown that the Description Logic language DL-Lite can be embedded in an extension of the Datalog language. The major extension consists in allowing existentially qualified variables in the heads of the rules.…”

Section: Knowledge Representation Formalismsmentioning

confidence: 99%

Active knowledge

Preda

Kasneci

Suchanek

et al. 2010

Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data

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The proliferation of knowledge-sharing communities and the advances in information extraction have enabled the construction of large knowledge bases using the RDF data model to represent entities and relationships. However, as the Web and its latently embedded facts evolve, a knowledge base can never be complete and up-to-date. On the other hand, a rapidly increasing suite of Web services provide access to timely and high-quality information, but this is encapsulated by the service interface. We propose to leverage the information that could be dynamically obtained from Web services in order to enrich RDF knowledge bases on the fly whenever the knowledge base does not suffice to answer a user query.To this end, we develop a sound framework for appropriately generating queries to encapsulated Web services and efficient algorithms for query execution and result integration. The query generator composes sequences of function calls based on the available service interfaces. As Web service calls are expensive, our method aims to reduce the number of calls in order to retrieve results with sufficient recall. Our approach is fully implemented in a complete prototype system named ANGIE 1 . The user can query and browse the RDF knowledge base as if it already contained all the facts from the Web services. This data, however, is gathered and integrated on the fly, transparently to the user. We demonstrate the viability and efficiency of our approach in experiments based on real-life data provided by popular Web services.

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“…To extract provenance paths using forward chaining the state of the art uses a chase graph [6] (also called derivation tree [1]). A chase graph is a directed graph consisting of a set of nodes representing the facts of the chase and having an arrow from a fact u to v iff v is obtained from u (possibly with other atoms) by the application of a rule in R.…”

Section: Introductionmentioning

confidence: 99%

“…Existential rules extend the Datalog language [7] with existential variables in the conclusion of the rules (also called tuple generating dependencies -TGDs) and generalise certain fragments of Description Logics by allowing n-ary predicates as well as cyclic structures [6]. We consider a first-order logical (FOL) language with constants but no other function symbol based on a vocabulary V composed of an infinite set of predicates, an infinite set of constants, an infinite set of variables and an infinite set of existential 'fresh' variables (called 'nulls', which act as placeholders for unknown constants).…”

Section: Introductionmentioning

confidence: 99%

On the Chase for All Provenance Paths with Existential Rules

Hecham

Bisquert²,

Croitoru

2017

Rules and Reasoning

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Abstract. In this paper we focus on the problem of how lineage for existential rules knowledge bases. Given a knowledge base and an atomic ground query, we want to output all minimal provenance paths of the query (i.e. the sequence of rule applications that generates an atom from a given set of facts). Obtaining all minimal provenance paths of a query using forward chaining can be challenging due to the simplifications done during the rule applications of different chase mechanisms. We build upon the notion of Graph of Atoms Dependency (GAD) and use it to solve the problem of provenance path loss in the context of forward chaining with existential rules. We study the properties of this structure and investigate how different chase mechanisms impact its construction.

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A general Datalog-based framework for tractable query answering over ontologies

Cited by 305 publications

References 69 publications

A better uncle for OWL

A better uncle for OWL

Active knowledge

On the Chase for All Provenance Paths with Existential Rules

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