Temporal Query Answering in the Description Logic DL-Lite

Borgwardt, Stefan; Lippmann, Marcel; Thost, Veronika

doi:10.1007/978-3-642-40885-4_11

Cited by 44 publications

(64 citation statements)

References 16 publications

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“…This offers a richer setting, yet without apparent application prospects in the context of existing temporal databases. The work in [5] is closer aligned with ours. It considers DL-Lite ontologies and studies a rewriting approach based on the use of standard CQ rewriting techniques.…”

Section: Related Worksupporting

confidence: 84%

“…The language TQL, presented in this section, is defined using a generic construction method for temporal query languages in DLs, explored in [10,3,5]. TQL is a combination of a temporal language with CQs, obtained by substituting CQs for the atoms in temporal formulas.…”

Section: Temporal Query Languagementioning

confidence: 99%

“…The research on temporal extensions to OBDA has been taken up only very recently by Borgwardt et al [3,5] and Artale et al [2]. In [3], the authors study the same prototypical scenario as addressed here, but focus on its more foundational aspects.…”

Section: Related Workmentioning

confidence: 99%

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Practical Querying of Temporal Data via OWL 2 QL and SQL:2011

Klarman¹

EPiC Series in Computing

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We develop a practical approach to querying temporal data stored in temporal SQL:2011 databases through the semantic layer of OWL 2 QL ontologies. An interval-based temporal query language (TQL), which we propose for this task, is defined via naturally characterizable combinations of temporal logic with conjunctive queries. This foundation warrants well-defined semantics and formal properties of TQL querying. In particular, we show that under certain mild restrictions the data complexity of query answering remains in AC 0 , i.e., as in the usual, nontemporal case. On the practical side, TQL is tailored specifically to offer maximum expressivity while preserving the possibility of reusing standard first-order rewriting techniques and tools for OWL 2 QL.

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Section: Related Worksupporting

confidence: 84%

Section: Temporal Query Languagementioning

confidence: 99%

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Practical Querying of Temporal Data via OWL 2 QL and SQL:2011

Klarman¹

EPiC Series in Computing

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“…We study the computational complexity of the decision problem related to temporal query answering, query entailment [9,10]; that is, the question whether the query can be deduced from the knowledge in the TKB (considering queries containing no answer variables). 1 We investigate both combined and data complexity.…”

Section: Complexity Resultsmentioning

confidence: 99%

Using Ontology-Based Data Access to Enable Context Recognition in the Presence of Incomplete Information

Thost

2017

Künstl Intell

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Classical OBDA regards however only a single moment [7], which means that information about time cannot be used for reasoning and is thus lost.In the doctoral dissertation [17], we therefore investigate temporal query languages that allow to access temporal data through classical ontologies. In particular, we study the computational complexity of temporal query answering regarding ontologies written in lightweight description logics, such as DL-Lite [2,11], which are known to allow for efficient reasoning in the atemporal setting and are successfully applied in practice.We also present a so-called rewritability result for ontology-based temporal query answering, which suggests ways for implementation. In this article, we present an overview of our results. They may guide the choice of a query language for temporal OBDA in data-intensive applications that require fast processing, such as context recognition. Ontology-Based Temporal Query AnsweringConsider the following example context: "a user watches a video, but, after a while, works with a text editor that hides the video window". In such a situation, the operating system could optimize resource consumption by decreasing quality parameters of the video. For recognizing the context, it has to be encoded into a temporal query and answered over data about different points in time (i.e., different system states). Ontologies can augment this approach by providing an abstract, user-friendly interface to the data and by stating general domain knowledge, which can be taken into account during query answering.The setting we focus on is depicted in Fig. 1. The temporal query addresses a temporal knowledge base (TKB) that consists of an ontology and fact bases. We specifically

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“…Recent efforts on temporalised [22,23] and streamified [24][25][26][27][28][29][30][31] OBDA systems provide first steps towards handling temporal and streaming data in industrial applications. However, none of these approaches satisfies the requirements of the Siemens use case: either there is no implemented and/or optimized engine or the engine is still not fully developed (see the benchmark tests in [32]).…”

Section: Starql Query Languagementioning

confidence: 99%

Semantic access to streaming and static data at Siemens

Kharlamov

Mailis

Mehdi

et al. 2017

Journal of Web Semantics

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We present a description and analysis of the data access challenge in Siemens Energy. We advocate Ontology Based Data Access (OBDA) as a suitable Semantic Web driven technology to address the challenge. We derive requirements for applying OBDA in Siemens, review existing OBDA systems and discuss their limitations with respect to the Siemens requirements. We then introduce the Optique platform as a suitable OBDA solution for Siemens. The platform is based on a number of novel techniques and components including a deployment module, BootOX for ontology and mapping bootstrapping, a query language STARQL that allows for a uniform querying of both streaming and static data, a highly optimised backend, ExaStream, for processing such data, and a query formulation interface, OptiqueVQS, that allows to formulate STARQL queries without prior knowledge of its formal syntax. Finally, we describe our installation and evaluation of the platform in Siemens.

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Temporal Query Answering in the Description Logic DL-Lite

Cited by 44 publications

References 16 publications

Practical Querying of Temporal Data via OWL 2 QL and SQL:2011

Practical Querying of Temporal Data via OWL 2 QL and SQL:2011

Using Ontology-Based Data Access to Enable Context Recognition in the Presence of Incomplete Information

Semantic access to streaming and static data at Siemens

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