000121832 001__ 121832
000121832 005__ 20230210153058.0
000121832 0247_ $$2doi$$a10.1007/978-3-319-50112-3_1
000121832 0248_ $$2sideral$$a96856
000121832 037__ $$aART-2016-96856
000121832 041__ $$aeng
000121832 100__ $$aGuclu, Isa
000121832 245__ $$aHow Can Reasoner Performance of ABox Intensive Ontologies Be Predicted?
000121832 260__ $$c2016
000121832 5060_ $$aAccess copy available to the general public$$fUnrestricted
000121832 5203_ $$aReasoner performance prediction of ontologies in OWL 2 language has been studied so far from different dimensions. One key aspect of these studies has been the prediction of how much time a particular task for a given ontology will consume. Several approaches have adopted different machine learning techniques to predict time consumption of ontologies already. However, these studies focused on capturing general aspects of the ontologies (i.e., mainly the complexity of their TBoxes), while paying little attention to ABox intensive ontologies. To address this issue, in this paper, we propose to improve the representativeness of ontology metrics by developing new metrics which focus on the ABox features of ontologies. Our experiments show that the proposed metrics contribute to overall prediction accuracy for all ontologies in general without causing side-effects.
000121832 536__ $$9info:eu-repo/grantAgreement/EC/FP7/286348/EU/Knowledge Driven Data Exploitation/K-DRIVE$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2013-46238-C4-4-R
000121832 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000121832 592__ $$a0.339$$b2016
000121832 593__ $$aComputer Science (miscellaneous)$$c2016$$dQ2
000121832 593__ $$aTheoretical Computer Science$$c2016$$dQ3
000121832 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000121832 700__ $$0(orcid)0000-0003-4239-8785$$aBobed Lisbona, Carlos$$uUniversidad de Zaragoza
000121832 700__ $$aPan, Jeff Z. Pan
000121832 700__ $$aKollingbaum, Martin J.
000121832 700__ $$aLi, Yuan-Fang
000121832 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000121832 773__ $$g10055 (2016), 3-14$$pLect. notes comput. sci.$$tLecture Notes in Computer Science$$x0302-9743
000121832 8564_ $$s488755$$uhttps://zaguan.unizar.es/record/121832/files/texto_completo.pdf$$yPostprint
000121832 8564_ $$s1495322$$uhttps://zaguan.unizar.es/record/121832/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000121832 909CO $$ooai:zaguan.unizar.es:121832$$particulos$$pdriver
000121832 951__ $$a2023-02-10-09:03:35
000121832 980__ $$aARTICLE