Abstract
Reasoner 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
Adapted from Natural Language Processing, basically, it consists in adding 1 to all the witnessed values of the concept expressions in the ontology.
- 3.
- 4.
You can find the code of the OntologyChopper at http://sid.cps.unizar.es/projects/OWL2Predictions/JIST16/.
References
Fokoue, A., Meneguzzi, F., Sensoy, M., Pan, J.Z.: Querying linked ontological data through distributed summarization. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI2012) (2011)
Bobed, C., Yus, R., Bobillo, F., Mena, E.: Semantic reasoning on mobile devices: do androids dream of efficient reasoners? J. Web Semant. 35(4), 167–183 (2015). ISSN 1570–8268, https://dx.doi.org/10.1016/j.websem.2015.09.002
Burton-Jones, A., Storey, V.C., Sugumaran, V., Ahluwalia, P.: A semiotic metrics suite for assessing the quality of ontologies. Data Knowl. Eng. 55, 84–102 (2005)
Dentler, K., Cornet, R., ten Teije, A., de Keizer, N.: Comparison of reasoners for large ontologies in the OWL 2 EL profile. Semant. Web 2, 71–87 (2011)
Glimm, B., Horrocks, I., Motik, B., Stoilos, G., Wang, Z.: Hermit: an OWL 2 reasoner. J. Autom. Reasoning 53, 245–269 (2014)
Grau, B.C., Horrocks, I., Motik, B., Parsia, B., Patel-Schneider, P.F., Sattler, U.: OWL 2: the next step for OWL. J. Web Sem. 6, 309–322 (2008)
Guclu, I., Li, Y.-F., Pan, J.Z., Kollingbaum, M.J.: Predicting energy consumption of ontology reasoning over mobile devices. In: Groth, P., Simperl, E., Gray, A., Sabou, M., Krötzsch, M., Lecue, F., Flöck, F., Gil, Y. (eds.) ISWC 2016. LNCS, vol. 9981, pp. 289–304. Springer, Heidelberg (2016). doi:10.1007/978-3-319-46523-4_18
Hogan, A., Pan, J.Z., Polleres, A., Ren, Y.: Scalable OWL 2 reasoning for linked data. In: Polleres, A., d’Amato, C., Arenas, M., Handschuh, S., Kroner, P., Ossowski, S., Patel-Schneider, P. (eds.) Reasoning Web 2011. LNCS, vol. 6848, pp. 250–325. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23032-5_5
Jolliffe, I.: Principal Component Analysis. Wiley StatsRef: Statistics Reference Online (2002)
Kang, Y.-B., Li, Y.-F., Krishnaswamy, S.: Predicting reasoning performance using ontology metrics. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 198–214. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35176-1_13
Kang, Y.-B., Li, Y.-F., Krishnaswamy, S.: A rigorous characterization of classification performance - a tale of four reasoners. In: ORE (2012)
Kang, Y.-B., Pan, J.Z., Krishnaswamy, S., Sawangphol, W., Li, Y.-F.: How long will it take? Accurate prediction of ontology reasoning performance. In: AAAI (2014)
Li, Y.-F., Kennedy, G., Ngoran, F., Wu, P., Hunter, J.: An ontology-centric architecture for extensible scientific data management systems. Future Gener. Comput. Syst. 29, 641–653 (2013)
Matentzoglu, N., Bail, S., Parsia, B.: A corpus of OWL DL ontologies. In: Proceedings DL13 (2013)
Maynard, D., Peters, W., Li, Y.: Metrics for evaluation of ontology-based information extraction (2006)
Pan, J.Z., Ren, Y., Zhao, Y.: Tractable approximate deduction for OWL. Artificial Intelligence 235, 95–155
Pan, J.Z., Staab, S., Amann, U., Ebert, J., Zhao, Y.: Ontology-Driven Software Development. Springer Publishing Company, Incorporated, Ontology-Driven Software (2012)
Pan, J.Z., Thomas, E., Ren, Y., Taylor., S.: Tractable fuzzy and crisp reasoning in ontology applications. In: IEEE Computational Intelligence Magazine (2012)
Ren, Y., Pan, J.Z., Lee, K.: Optimising parallel ABox reasoning of EL ontologies. In: Description Logics (2012)
Ren, Y., Pan, J.Z., Zhao, Y.: Soundness preserving approximation for TBox reasoning. In: AAAI (2010)
Romero, A.A., Grau, B.C., Horrocks, I.: More: modular combination of OWL reasoners for ontology classification. In: SEMWEB (2012)
Sazonau, V., Sattler, U., Brown, G.: Predicting performance of OWL reasoners: locally or globally? In: Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, KR 2014, Vienna, Austria, July 20–24, 2014 (2014)
Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: a practical OWL-DL reasoner. J. Web Sem. 5, 51–53 (2007)
Thomas, E., Pan, J.Z., Ren, Y.: TrOWL: tractable OWL 2 reasoning infrastructure. In: Aroyo, L., Antoniou, G., Hyvönen, E., Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010. LNCS, vol. 6089, pp. 431–435. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13489-0_38
Tsarkov, D., Horrocks, I.: FaCT++ description logic reasoner: system description. In: Furbach, U., Shankar, N. (eds.) IJCAR 2006. LNCS (LNAI), vol. 4130, pp. 292–297. Springer, Heidelberg (2006). doi:10.1007/11814771_26
Yao, H., Orme, A.M., Etzkorn, L.: Cohesion metrics for ontology design and application. J. Comput. Sci. 1, 107–113 (2005)
Yuan Ren, J.Z.P., Lee, K.: Optimising parallel ABox reasoning of el ontologies. In: Proceedings of the 25th International Workshop on Description Logics (DL2012) (2012)
Zhang, H., Li, Y.-F., Tan, H.B.K.: Measuring design complexity of semantic web ontologies. J. Syst. Softw. 83, 803–814 (2010)
Acknowledgments
This work was partially supported by the EC Marie Curie K-Drive project (286348), the CICYT project (TIN2013-46238-C4-4-R) and the DGA-FSE project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Guclu, I., Bobed, C., Pan, J.Z., Kollingbaum, M.J., Li, YF. (2016). How Can Reasoner Performance of ABox Intensive Ontologies Be Predicted?. In: Li, YF., et al. Semantic Technology. JIST 2016. Lecture Notes in Computer Science(), vol 10055. Springer, Cham. https://doi.org/10.1007/978-3-319-50112-3_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-50112-3_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50111-6
Online ISBN: 978-3-319-50112-3
eBook Packages: Computer ScienceComputer Science (R0)