LTCS – Report On Implementing Temporal Query Answering in DL-Lite

Ontology-based data access augments classical query answering over fact bases by adopting the open-world assumption and by including domain knowledge provided by an ontology. We implemented temporal query answering w.r.t. ontologies formulated in the Description Logic DL-Lite. Focusing on temporal conjunctive queries (TCQs), which combine conjunctive queries via the operators of propositional linear temporal logic, we regard three approaches for answering them: an iterative algorithm that considers all data available; a window-based algorithm; and a rewriting approach, which translates the TCQs to be answered into SQL queries. Since the relevant ontological knowledge is already encoded into the latter queries, they can be answered by a standard database system. Our evaluation especially shows that implementations of both the iterative and the window-based algorithm answer TCQs within a few milliseconds, and that the former achieves a constant performance, even if data is growing over time.

[1]  Franz Baader,et al.  Temporalizing Ontology-Based Data Access , 2013, CADE.

[2]  Sina Samangooei Stream Reasoning with Linked Data , 2013 .

[3]  Samantha Bail,et al.  OWL Reasoner Evaluation (ORE) Workshop 2013 Results: Short Report , 2013, ORE.

[4]  Ralf Möller,et al.  A Stream-Temporal Query Language for Ontology Based Data Access , 2014, Description Logics.

[5]  Diego Calvanese,et al.  High Performance Query Answering over DL-Lite Ontologies , 2012, KR.

[6]  Diego Calvanese,et al.  DL-Lite: Tractable Description Logics for Ontologies , 2005, AAAI.

[7]  Franz Baader,et al.  LTL over description logic axioms , 2008, TOCL.

[8]  Carsten Lutz,et al.  Temporalising Tractable Description Logics , 2007, 14th International Symposium on Temporal Representation and Reasoning (TIME'07).

[9]  S. Handschuh,et al.  Reasoning Web. Semantic Technologies for Information Systems , 2009 .

[10]  Alessandro Artale,et al.  A Cookbook for Temporal Conceptual Data Modelling with Description Logics , 2012, TOCL.

[11]  Sebastian Rudolph,et al.  Stream reasoning and complex event processing in ETALIS , 2012, Semantic Web.

[12]  Ying Zhang,et al.  SRBench: A Streaming RDF/SPARQL Benchmark , 2012, SEMWEB.

[13]  Jean Christoph Jung,et al.  Lightweight Description Logics and Branching Time: A Troublesome Marriage , 2014, KR.

[14]  Stefan Borgwardt,et al.  Temporalizing rewritable query languages over knowledge bases , 2015, J. Web Semant..

[15]  Karl Aberer,et al.  Enabling Query Technologies for the Semantic Sensor Web , 2012, Int. J. Semantic Web Inf. Syst..

[16]  Thomas Andreas Meyer,et al.  Querying Temporal Databases via OWL 2 QL , 2014, RR.

[17]  Franz Baader,et al.  Temporal query entailment in the Description Logic SHQ , 2015, J. Web Semant..

[18]  Ashok K. Chandra,et al.  Optimal implementation of conjunctive queries in relational data bases , 1977, STOC '77.

[19]  Diego Calvanese,et al.  Ontologies and Databases: The DL-Lite Approach , 2009, Reasoning Web.

[20]  Alessandro Margara,et al.  TESLA: a formally defined event specification language , 2010, DEBS '10.