Alaya, N.; Yahia, S.B.; Lamolle, M.: Ranking with ties of OWL ontology reasoners based on learned performances (2016)
0.00
0.0024161388 = product of:
0.007248416 = sum of:
0.007248416 = product of:
0.014496832 = sum of:
0.014496832 = weight(_text_:of in 3378) [ClassicSimilarity], result of:
0.014496832 = score(doc=3378,freq=12.0), product of:
0.06850986 = queryWeight, product of:
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.043811057 = queryNorm
0.21160212 = fieldWeight in 3378, product of:
3.4641016 = tf(freq=12.0), with freq of:
12.0 = termFreq=12.0
1.5637573 = idf(docFreq=25162, maxDocs=44218)
0.0390625 = fieldNorm(doc=3378)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Abstract
- Over the last decade, several ontology reasoners have been proposed to overcome the computational complexity of inference tasks on expressive ontology languages such as OWL 2 DL. Nevertheless, it is well-accepted that there is no outstanding reasoner that can outperform in all input ontologies. Thus, deciding the most suitable reasoner for an ontology based application is still a time and effort consuming task. In this paper, we suggest to develop a new system to provide user support when looking for guidance over ontology reasoners. At first, we will be looking at automatically predict a single reasoner empirical performances, in particular its robustness and efficiency, over any given ontology. Later, we aim at ranking a set of candidate reasoners in a most preferred order by taking into account information regarding their predicted performances. We conducted extensive experiments covering over 2500 well selected real-world ontologies and six state-of-the-art of the most performing reasoners. Our primary prediction and ranking results are encouraging and witnessing the potential benefits of our approach.
