@conference {133, title = {All But Not Nothing: Left-Hand Side Universals for Tractable {OWL} Profiles}, booktitle = {Proceedings of the 11th International Workshop on OWL: Experiences and Directions (OWLED 2014) co-located with 13th International Semantic Web Conference on (ISWC 2014), Riva del Garda, Italy, October 17-18, 2014.}, volume = {1265}, year = {2014}, month = {10/2014}, pages = {97-108}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {We show that occurrences of the universal quantifier in the left-hand side of general concept inclusions can be rewritten into EL++ axioms under certain circumstances. I.e., this intuitive modeling feature is available for OWL EL while retaining tractability. Furthermore, this rewriting makes it possible to reason over corresponding extensions of EL++ and Horn-SROIQ using standard reasoners.}, keywords = {description logics, Horn Logics, OWL}, url = {http://ceur-ws.org/Vol-1265/owled2014_submission_13.pdf}, author = {David Carral and Adila Krisnadhi and Sebastian Rudolph and Pascal Hitzler}, editor = {C. Maria Keet and Valentina A. M. Tamma} } @conference {135, title = {EL-ifying Ontologies}, booktitle = {Automated Reasoning - 7th International Joint Conference, IJCAR 2014, Held as Part of the Vienna Summer of Logic, {VSL} 2014, Vienna, Austria, July 19-22, 2014. Proceedings}, year = {2014}, pages = {464{\textendash}479}, abstract = {

The OWL 2 profiles are fragments of the ontology language OWL 2 for which standard reasoning tasks are feasible in polynomial time. Many OWL ontologies, however, contain a typically small number of out-of-profile axioms, which may have little or no influence on reasoning outcomes. We investigate techniques for rewriting axioms into the EL and RL profiles of OWL 2. We have tested our techniques on both classification and data reasoning tasks with encouraging results.

}, keywords = {description logics, OWL, Rewriting, Tractable Reasoning}, doi = {10.1007/978-3-319-08587-6_36}, url = {http://dx.doi.org/10.1007/978-3-319-08587-6_36}, author = {David Carral and Cristina Feier and Cuenca Grau, Bernardo and Pascal Hitzler and Ian Horrocks} } @conference {131, title = {Pushing the Boundaries of Tractable Ontology Reasoning}, booktitle = {The Semantic Web - ISWC 2014 - 13th International Semantic Web Conference, Riva del Garda, Italy, October 19-23, 2014. Proceedings, Part II}, year = {2014}, pages = {148{\textendash}163}, abstract = {

We identify a class of Horn ontologies for which standard reasoning tasks such as instance checking and classification are tractable. The class is general enough to include the OWL 2 EL, QL, and RL profiles. Verifying whether a Horn ontology belongs to the class can be done in polynomial time. We show empirically that the class includes many real-world ontologies that are not included in any OWL 2 profile, and thus that polynomial time reasoning is possible for these ontologies.

}, keywords = {description logics, OWL, Tractable Reasoning}, doi = {10.1007/978-3-319-11915-1_10}, url = {http://dx.doi.org/10.1007/978-3-319-11915-1_10}, author = {David Carral and Cristina Feier and Cuenca Grau, Bernardo and Pascal Hitzler and Ian Horrocks} } @conference {134, title = {Is Your Ontology as Hard as You Think? Rewriting Ontologies into Simpler DLs}, booktitle = {Informal Proceedings of the 27th International Workshop on Description Logics, Vienna, Austria, July 17-20, 2014.}, year = {2014}, pages = {128{\textendash}140}, abstract = {

We investigate cases where an ontology expressed in a seemingly hard DL can be polynomially reduced to one in a simpler logic, while preserving reasoning outcomes for classification and fact entailment. Our transformations target the elimination of inverse roles, universal and existential restrictions, and in the best case allow us to rewrite the given ontology into one of the OWL 2 profiles. Even if an ontology cannot be fully rewritten into a profile, in many cases our transformations allow us to exploit further optimisation techniques. Moreover, the elimination of some out-of-profile axioms can improve the performance of modular reasoners, such as MORe. We have tested our techniques on both classification and data reasoning tasks with encouraging results.

}, keywords = {description logics, OWL, Tractable Reasoning}, url = {http://ceur-ws.org/Vol-1193/paper_75.pdf}, author = {David Carral and Cristina Feier and Ana Armas Romero and Cuenca Grau, Bernardo and Pascal Hitzler and Ian Horrocks} } @conference {10, title = {DistEL: A Distributed EL+ Ontology Classifier}, booktitle = {Proceedings of the 9th International Workshop on Scalable Semantic Web Knowledge Base Systems, co-located with the International Semantic Web Conference (ISWC 2013)}, volume = {1046}, year = {2013}, month = {10/2013}, pages = {17-32}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Sydney, Australia}, abstract = {OWL 2 EL ontologies are used to model and reason over data from diverse domains such as biomedicine, geography and road traffic. Data in these domains is increasing at a rate quicker than the increase in main memory and computation power of a single machine. Recent efforts in OWL reasoning algorithms lead to the decrease in classification time from several hours to a few seconds even for large ontologies like SNOMED CT. This is especially true for ontologies in the description logic EL+ (a fragment of the OWL 2 EL profile). Reasoners such as Pellet, Hermit, ELK etc. make an assumption that the ontology would fit in the main memory, which is unreasonable given projected increase in data volumes. Increase in the data volume also necessitates an increase in the computation power. This lead us to the use of a distributed system, so that memory and computation requirements can be spread across machines. We present a distributed system for the classification of EL+ ontologies along with some results on its scalability and performance.}, keywords = {Classification, DistEL, Distributed Reasoning, EL+, OWL, Scalability}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti}, editor = {Thorsten Liebig and Achille Fokoue} } @conference {130, title = {A Geo-ontology Design Pattern for Semantic Trajectories}, booktitle = {Spatial Information Theory - 11th International Conference, COSIT 2013, Scarborough, UK, September 2-6, 2013. Proceedings}, year = {2013}, pages = {438{\textendash}456}, abstract = {

Trajectory data have been used in a variety of studies, including human behavior analysis, transportation management, and wildlife tracking. While each study area introduces a different perspective, they share the need to integrate positioning data with domain-specific information. Semantic annotations are necessary to improve discovery, reuse, and integration of trajectory data from different sources. Consequently, it would be beneficial if the common structure encountered in trajectory data could be annotated based on a shared vocabulary, abstracting from domain-specific aspects. Ontology design patterns are an increasingly popular approach to define such flexible and self-contained building blocks of annotations. They appear more suitable for the annotation of interdisciplinary, multi-thematic, and multi-perspective data than the use of foundational and domain ontologies alone. In this paper, we introduce such an ontology design pattern for semantic trajectories. It was developed as a community effort across multiple disciplines and in a data-driven fashion. We discuss the formalization of the pattern using the Web Ontology Language (OWL) and apply the pattern to two different scenarios, personal travel and wildlife monitoring.

}, keywords = {Ontology Design Pattern, OWL, Trajectory}, doi = {10.1007/978-3-319-01790-7_24}, url = {http://dx.doi.org/10.1007/978-3-319-01790-7_24}, author = {Yingjie Hu and Krzysztof Janowicz and David Carral and Simon Scheider and Werner Kuhn and Gary Berg-Cross and Pascal Hitzler and Mike Dean and Dave Kolas} } @conference {129, title = {An Ontology Design Pattern for Cartographic Map Scaling}, booktitle = {The Semantic Web: Semantics and Big Data, 10th International Conference, ESWC 2013, Montpellier, France, May 26-30, 2013. Proceedings}, volume = {7882}, year = {2013}, pages = {76{\textendash}93}, publisher = {Springer}, organization = {Springer}, abstract = {

The concepts of scale is at the core of cartographic abstraction and mapping. It defines which geographic phenomena should be displayed, which type of geometry and map symbol to use, which measures can be taken, as well as the degree to which features need to be exaggerated or spatially displaced. In this work, we present an ontology design pattern for map scaling using the Web Ontology Language (OWL) within a particular extension of the OWL RL profile. We explain how it can be used to describe scaling applications, to reason over scale levels, and geometric representations. We propose an axiomatization that allows us to impose meaningful constraints on the pattern, and, thus, to go beyond simple surface semantics. Interestingly, this includes several functional constraints currently not expressible in any of the OWL profiles. We show that for this specific scenario, the addition of such constraints does not increase the reasoning complexity which remains tractable.

}, keywords = {Map Scaling, Ontology Design Patterns, OWL}, doi = {10.1007/978-3-642-38288-8_6}, url = {http://dx.doi.org/10.1007/978-3-642-38288-8_6}, author = {David Carral and Simon Scheider and Krzysztof Janowicz and Charles Vardeman and Adila Krisnadhi and Pascal Hitzler}, editor = {Philipp Cimiano and {\'O}scar Corcho and Valentina Presutti and Laura Hollink and Sebastian Rudolph} } @article {61, title = {Paraconsistent OWL and Related Logics}, journal = {Semantic Web}, volume = {4}, year = {2013}, pages = {395{\textendash}427}, abstract = {The Web Ontology Language OWL is currently the most prominent formalism for representing ontologies in Semantic Web applications. OWL is based on description logics, and automated reasoners are used to infer knowledge implicitly present in OWL ontologies. However, because typical description logics obey the classical principle of explosion, reasoning over inconsistent ontologies is impossible in OWL. This is so despite the fact that inconsistencies are bound to occur in many realistic cases, e.g., when multiple ontologies are merged or when ontologies are created by machine learning or data mining tools. In this paper, we present four-valued paraconsistent description logics which can reason over inconsistencies. We focus on logics corresponding to OWL DL and its profiles. We present the logic SROIQ4, showing that it is both sound relative to classical SROIQ and that its embedding into SROIQ is consequence preserving. We also examine paraconsistent varieties of EL++, DL-Lite, and Horn-DLs. The general framework described here has the distinct advantage of allowing classical reasoners to draw sound but nontrivial conclusions from even inconsistent knowledge bases. Truth-value gaps and gluts can also be selectively eliminated from models (by inserting additional axioms into knowledge bases). If gaps but not gluts are eliminated, additional classical conclusions can be drawn without affecting paraconsistency.}, keywords = {Automated Deduction, Complexity, Description Logic, OWL, Paraconsistency, Semantic Web, Web Ontology Language}, doi = {10.3233/SW-2012-0066}, url = {http://dx.doi.org/10.3233/SW-2012-0066}, author = {Frederick Maier and Yue Ma and Pascal Hitzler} } @conference {127, title = {Extending Description Logic Rules}, booktitle = {The Semantic Web: Research and Applications - 9th Extended Semantic Web Conference, ESWC 2012, Heraklion, Crete, Greece, May 27-31, 2012. Proceedings}, year = {2012}, pages = {345{\textendash}359}, abstract = {

Description Logics {\textendash} the logics underpinning the Web Ontology Language OWL {\textendash} and rules are currently the most prominent paradigms used for modeling knowledge for the Semantic Web. While both of these approaches are based on classical logic, the paradigms also differ significantly, so that naive combinations result in undesirable properties such as undecidability. Recent work has shown that many rules can in fact be expressed in OWL. In this paper we extend this work to include some types of rules previously excluded. We formally define a set of first order logic rules, C-Rules, which can be expressed within OWL extended with role conjunction. We also show that the use of nominal schemas results in even broader coverage.

}, keywords = {description logics, OWL, Rules}, doi = {10.1007/978-3-642-30284-8_30}, url = {http://dx.doi.org/10.1007/978-3-642-30284-8_30}, author = {David Carral and Pascal Hitzler} } @conference {126, title = {A logical geo-ontology design pattern for quantifying over types}, booktitle = {SIGSPATIAL 2012 International Conference on Advances in Geographic Information Systems (formerly known as GIS), SIGSPATIAL{\textquoteright}12, Redondo Beach, CA, USA, November 7-9, 2012}, year = {2012}, pages = {239{\textendash}248}, keywords = {Biodiversity, description logics, Ontology Design Patterns, OWL}, doi = {10.1145/2424321.2424352}, url = {http://doi.acm.org/10.1145/2424321.2424352}, author = {David Carral and Krzysztof Janowicz and Pascal Hitzler} } @conference {124, title = {Recent Advances in Integrating {OWL} and Rules}, booktitle = {Web Reasoning and Rule Systems - 6th International Conference, RR 2012, Vienna, Austria, September 10-12, 2012. Proceedings}, volume = {7497}, year = {2012}, month = {09/2012}, pages = {225-228}, publisher = {Springer}, organization = {Springer}, address = {Austria, Vienna}, abstract = {As part of the quest for a unifying logic for the Semantic Web Technology Stack, a central issue is finding suitable ways of integrating description logics based on the Web Ontology Language (OWL) with rule-based approaches based on logic programming. Such integration is difficult since naive approaches typically result in the violation of one or more desirable design principles. For example, while both OWL 2 DL and RIF Core (a dialect of the Rule Interchange Format RIF) are decidable, their naive union is not, unless carefully chosen syntactic restrictions are applied. We report on recent advances and ongoing work by the authors in integrating OWL and rulesWe take an OWL-centric perspective, which means that we take OWL 2 DL as a starting point and pursue the question of how features of rulebased formalisms can be added without jeopardizing decidability. We also report on incorporating the closed world assumption and on reasoning algorithms. This paper essentially serves as an entry point to the original papers, to which we will refer throughout, where detailed expositions of the results can be found.}, keywords = {description logics, OWL, Rules}, doi = {10.1007/978-3-642-33203-6_20}, url = {http://dx.doi.org/10.1007/978-3-642-33203-6_20}, author = {Matthias Knorr and David Carral and Pascal Hitzler and Adila Krisnadhi and Frederick Maier and Cong Wang}, editor = {Markus Kr{\"o}tzsch and Umberto Straccia} } @article {80, title = {Concept learning in description logics using refinement operators}, journal = {Machine Learning}, volume = {78}, year = {2010}, pages = {203{\textendash}250}, abstract = {

With the advent of the Semantic Web, description logics have become one of the most prominent paradigms for knowledge representation and reasoning. Progress in research and applications, however, is constrained by the lack of well-structured knowledge bases consisting of a sophisticated schema and instance data adhering to this schema. It is paramount that suitable automated methods for their acquisition, maintenance, and evolution will be developed. In this paper, we provide a learning algorithm based on refinement operators for the description logic ALCQ including support for concrete roles. We develop the algorithm from thorough theoretical foundations by identifying possible abstract property combinations which refinement operators for description logics can have. Using these investigations as a basis, we derive a practically useful complete and proper refinement operator. The operator is then cast into a learning algorithm and evaluated using our implementation DL-Learner. The results of the evaluation show that our approach is superior to other learning approaches on description logics, and is competitive with established ILP systems.

}, keywords = {description logics, Inductive logic programming, OWL, refinement operators, Semantic Web, Structured Machine Learning}, url = {http://springerlink.metapress.com/content/c040n45u15qrnu44/}, author = {Jens Lehmann and Pascal Hitzler} }