01448nas a2200217 4500008004100000245005100041210005000092260004800142300001000190490000900200520077600209653002600985653001101011653001601022100002301038700002001061700002201081700002101103700002001124856008601144 2014 eng d00aDistributed OWL EL Reasoning: The Story So Far0 aDistributed OWL EL Reasoning The Story So Far aRiva del Garda, ItalybCEUR-WS.orgc10/2014 a61-760 v12613 a
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).
10aDistributed Reasoning10aOWL EL10aScalability1 aMutharaju, Raghava1 aHitzler, Pascal1 aMateti, Prabhaker1 aLiebig, Thorsten1 aFokoue, Achille uhttps://daselab.cs.ksu.edu/publications/distributed-owl-el-reasoning-story-so-far01783nas a2200253 4500008003900000245005000039210004800089260004400137300001000181490000900191520104900200653001901249653001101268653002601279653000801305653000801313653001601321100002301337700002001360700002201380700002101402700002001423856008601443 2013 d00aDistEL: A Distributed EL+ Ontology Classifier0 aDistEL A Distributed EL Ontology Classifier aSydney, AustraliabCEUR-WS.orgc10/2013 a17-320 v10463 aOWL 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.10aClassification10aDistEL10aDistributed Reasoning10aEL+10aOWL10aScalability1 aMutharaju, Raghava1 aHitzler, Pascal1 aMateti, Prabhaker1 aLiebig, Thorsten1 aFokoue, Achille uhttps://daselab.cs.ksu.edu/publications/distel-distributed-el-ontology-classifier00941nas a2200289 4500008004100000245008400041210006900125260001300194300001400207490000900221100001600230700002400246700002000270700002000290700002000310700001800330700001800348700002000366700002000386700001900406700003000425700001600455700002100471700001700492700002400509856011800533 2013 eng d00aA Linked-Data-Driven and Semantically-Enabled Journal Portal for Scientometrics0 aLinkedDataDriven and SemanticallyEnabled Journal Portal for Scie bSpringer a114–1290 v82191 aHu, Yingjie1 aJanowicz, Krzysztof1 aMcKenzie, Grant1 aSengupta, Kunal1 aHitzler, Pascal1 aAlani, Harith1 aKagal, Lalana1 aFokoue, Achille1 aGroth, Paul, T.1 aBiemann, Chris1 aParreira, Josiane, Xavier1 aAroyo, Lora1 aNoy, Natasha, F.1 aWelty, Chris1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/linked-data-driven-and-semantically-enabled-journal-portal-scientometrics