Query Answering for Rough EL Ontologies

Querying large datasets with incomplete and vague data is still a challenge. Ontology-based query answering extends standard database query answering by background knowledge from an ontology to augment incomplete data. We focus on ontologies written in rough description logics (DLs), which allow to represent vague knowledge by partitioning the domain of discourse into classes of indiscernible elements. In this paper, we extend the combined approach for ontologybased query answering to a variant of the DL ELH⊥ augmented with rough concept constructors. We show that this extension preserves the good computational properties of classical EL and can be implemented by standard database

[1]  Rafael Peñaloza,et al.  The complexity of fuzzy EL under the Łukasiewicz T-norm , 2017, Int. J. Approx. Reason..

[2]  Rafael Peñaloza,et al.  The limits of decidability in fuzzy description logics with general concept inclusions , 2015, Artif. Intell..

[3]  Frank Beer,et al.  An In-Database Rough Set Toolkit , 2015, LWA.

[4]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[5]  Thomas Lukasiewicz,et al.  Representing Uncertain Concepts in Rough Description Logics via Contextual Indiscernibility Relations , 2008, URSW.

[6]  Michel C. A. Klein,et al.  Description Logics with Approximate Definitions - Precise Modeling of Vague Concepts , 2007, IJCAI.

[7]  Michel C. A. Klein,et al.  Rough Description Logics for Modeling Uncertainty in Instance Unification , 2007, URSW.

[8]  Boris Motik,et al.  Answering Conjunctive Queries over EL Knowledge Bases with Transitive and Reflexive Roles , 2014, AAAI.

[9]  Franz Baader,et al.  Adding Threshold Concepts to the Description Logic EL , 2015, Description Logics.

[10]  Rafael Peñaloza,et al.  Roughening the EL Envelope , 2013 .

[11]  Rafael Peñaloza,et al.  Query Answering for Rough EL Ontologies (Extended Technical Report) , 2018, ArXiv.

[12]  C. Maria Keet Ontology Engineering with Rough Concepts and Instances , 2010, EKAW.

[13]  Theresa Beaubouef,et al.  Rough Sets , 2019, Lecture Notes in Computer Science.

[14]  Umberto Straccia,et al.  Managing uncertainty and vagueness in description logics for the Semantic Web , 2008, J. Web Semant..

[15]  Franz Baader,et al.  Pushing the EL Envelope , 2005, IJCAI.

[16]  Tsau Young Lin,et al.  Rough Sets and Data Mining: Analysis of Imprecise Data , 1996 .

[17]  Frank van Harmelen,et al.  A Contextualised Semantics for owl: sameAs , 2016, ESWC.

[18]  Carsten Lutz,et al.  Deciding inseparability and conservative extensions in the description logic EL , 2010, J. Symb. Comput..

[19]  Carsten Lutz,et al.  Conjunctive Query Answering in the Description Logic EL Using a Relational Database System , 2009, IJCAI.

[20]  Carsten Lutz,et al.  The Combined Approach to OBDA: Taming Role Hierarchies using Filters , 2012, SSWS+HPCSW@ISWC.

[21]  Suqin Tang,et al.  Reasoning with rough description logics: An approximate concepts approach , 2008, Information Sciences.

[22]  García-Cerdaña Fuzzy Description Logic , 2017 .

[23]  Tsau Young Lin,et al.  A New Rough Sets Model Based on Database Systems , 2003, Fundam. Informaticae.

[24]  Yavor Nenov,et al.  Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF Systems , 2014, AAAI.

[25]  Zdzislaw Pawlak,et al.  Reasoning about Data - A Rough Set Perspective , 1998, Rough Sets and Current Trends in Computing.

[26]  Sebastian Rudolph,et al.  Query Answering in the Horn Fragments of the Description Logics SHOIQ and SROIQ , 2011, IJCAI.

[27]  Rafael Peñaloza,et al.  Similarity-based relaxed instance queries , 2015, J. Appl. Log..

[28]  C. Maria Keet Rough subsumption reasoning with rOWL , 2011, SAICSIT '11.