@conference {156, title = {Distributed and Scalable OWL EL Reasoning}, booktitle = {Proceedings of the 12th Extended Semantic Web Conference (ESWC 2015) }, year = {2015}, publisher = {Springer}, organization = {Springer}, address = {Portoroz, Slovenia}, abstract = {

OWL 2 EL is one of the tractable proles of the Web Ontology\ Language (OWL) which is a W3C-recommended standard. OWL 2\ EL provides sucient expressivity to model large biomedical ontologies\ as well as streaming data such as trac, while at the same time allows\ for ecient reasoning services. Existing reasoners for OWL 2 EL, however,\ use only a single machine and are thus constrained by memory and\ computational power. At the same time, the automated generation of\ ontological information from streaming data and text can lead to very\ large ontologies which can exceed the capacities of these reasoners. We\ thus describe a distributed reasoning system that scales well using a cluster\ of commodity machines. We also apply our system to a use case on\ city trac data and show that it can handle volumes which cannot be\ handled by current single machine reasoners.

}, keywords = {DistEL, Distributed Reasoning, Ontology Classification, OWL EL}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti and Freddy L{\'e}cu{\'e}} } @conference {11, title = {Distributed OWL EL Reasoning: The Story So Far}, booktitle = {Proceedings of the 10th International Workshop on Scalable Semantic Web Knowledge Base Systems, Riva Del Garda, Italy}, volume = {1261}, year = {2014}, month = {10/2014}, pages = {61-76}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Riva del Garda, Italy}, abstract = {

Automated generation of axioms from streaming data, such as traffic and text, can result in very large ontologies that single machine reasoners cannot handle. Reasoning with large ontologies requires distributed solutions. Scalable reasoning techniques for RDFS, OWL Horst and OWL 2 RL now exist. For OWL 2 EL, several distributed reasoning approaches have been tried, but are all perceived to be inefficient. We analyze this perception. We analyze completion rule based distributed approaches, using different characteristics, such as dependency among the rules, implementation optimizations, how axioms and rules are distributed. We also present a distributed queue approach for the classification of ontologies in description logic EL+ (fragment of OWL 2 EL).

}, keywords = {Distributed Reasoning, OWL EL, Scalability}, author = {Raghava Mutharaju and Pascal Hitzler and Prabhaker Mateti}, editor = {Thorsten Liebig and Achille Fokoue} } @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 {159, title = {D-SPARQ: Distributed, Scalable and Efficient RDF Query Engine}, booktitle = {Proceedings of the ISWC 2013 Posters \& Demonstrations Track}, volume = {1035}, year = {2013}, pages = {261{\textendash}264}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, address = {Sydney, Australia}, abstract = {

We present D-SPARQ, a distributed RDF query engine that combines the MapReduce processing framework with a NoSQL distributed data store, MongoDB. The performance of processing SPARQL queries mainly depends on the efficiency of handling the join operations between the RDF triple patterns. Our system features two unique characteristics that enable efficiently tackling this challenge: 1) Identifying specific patterns of the input queries that enable improving the performance by running different parts of the query in a parallel mode. 2) Using the triple selectivity information for reordering the individual triples of the input query within the identified query patterns. The preliminary results demonstrate the scalability and efficiency of our distributed RDF query engine.

}, keywords = {D-SPARQ, Distributed Querying, Scalable RDF querying, SPARQL}, url = {http://ceur-ws.org/Vol-1035/iswc2013_poster_21.pdf}, author = {Raghava Mutharaju and Sherif Sakr and Alessandra Sala and Pascal Hitzler}, editor = {Eva Blomqvist and Tudor Groza} } @conference {160, title = {Very Large Scale OWL Reasoning through Distributed Computation}, booktitle = {11th International Semantic Web Conference (ISWC 2012), Proceedings, Part II}, volume = {7650}, year = {2012}, pages = {407{\textendash}414}, publisher = {Springer}, organization = {Springer}, address = {Boston, MA, USA}, abstract = {

Due to recent developments in reasoning algorithms of the\ various OWL profiles, the classification time for an ontology has come\ down drastically. For all of the popular reasoners, in order to process\ an ontology, an implicit assumption is that the ontology should fit in\ primary memory. The memory requirements for a reasoner are already\ quite high, and considering the ever increasing size of the data to be\ processed and the goal of making reasoning Web scale, this assumption\ becomes overly restrictive. In our work, we study several distributed\ classification approaches for the description logic EL+ (a fragment of OWL 2 EL profile). We present the lessons learned from each approach, our current results, and plans for future work.

}, keywords = {Distributed Reasoning, Ontology Classification, OWL EL}, doi = {10.1007/978-3-642-35173-0_30}, url = {http://dx.doi.org/10.1007/978-3-642-35173-0_30}, author = {Raghava Mutharaju}, editor = {Philippe Cudr{\'e}-Mauroux and Jeff Heflin and Evren Sirin and Tania Tudorache and J{\'e}r{\^o}me Euzenat and Manfred Hauswirth and Josiane Xavier Parreira and James A. Hendler and Guus Schreiber and Abraham Bernstein and Eva Blomqvist} }