Abstract
In the context of the Semantic Web, learning implicit knowledge in terms of axioms from Linked Open Data has been the object of much current research. In this paper, we propose a method based on grammar-based genetic programming to automatically discover disjointness axioms between concepts from the Web of Data. A training-testing model is also implemented to overcome the lack of benchmarks and comparable research. The acquisition of axioms is performed on a small sample of DBpedia with the help of a Grammatical Evolution algorithm. The accuracy evaluation of mined axioms is carried out on the whole DBpedia. Experimental results show that the proposed method gives high accuracy in mining class disjointness axioms involving complex expressions.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
Available for download at http://bit.ly/2OtFqHp.
- 3.
- 4.
- 5.
References
Guarino, N., Oberle, D., Staab, S.: What is an ontology? In: Staab, S., Studer, R. (eds.) Handbook on Ontologies. IHIS, pp. 1–17. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-92673-3_0
Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing? Int. J. Hum. Comput. Stud. 43(5–6), 907–928 (1995)
Zhu, M.: DC proposal: ontology learning from noisy linked data. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7032, pp. 373–380. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25093-4_31
Völker, J., Vrandečić, D., Sure, Y., Hotho, A.: Learning disjointness. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 175–189. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72667-8_14
Völker, J., Fleischhacker, D., Stuckenschmidt, H.: Automatic acquisition of class disjointness. J. Web Semant. 35, 124–139 (2015)
Lehmann, J.: Dl-learner: learning concepts in description logics. J. Mach. Learn. Res. 10, 2639–2642 (2009)
Reynaud, J., Toussaint, Y., Napoli, A.: Redescription mining for learning definitions and disjointness axioms in linked open data. In: Endres, D., Alam, M., Şotropa, D. (eds.) ICCS 2019. LNCS (LNAI), vol. 11530, pp. 175–189. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23182-8_13
Rizzo, G., d’Amato, C., Fanizzi, N., Esposito, F.: Terminological cluster trees for disjointness axiom discovery. In: Blomqvist, E., Maynard, D., Gangemi, A., Hoekstra, R., Hitzler, P., Hartig, O. (eds.) ESWC 2017. LNCS, vol. 10249, pp. 184–201. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58068-5_12
Nguyen, T.H., Tettamanzi, A.G.B.: Learning class disjointness axioms using grammatical evolution. In: Sekanina, L., Hu, T., Lourenço, N., Richter, H., García-Sánchez, P. (eds.) EuroGP 2019. LNCS, vol. 11451, pp. 278–294. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16670-0_18
Nguyen, T.H., Tettamanzi, A.G.B.: An evolutionary approach to class disjointness axiom discovery. In: WI, pp. 68–75. ACM (2019)
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Vanneschi, L., Poli, R.: Genetic programming – introduction, applications, theory and open issues. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds.) Handbook of Natural Computing, pp. 709–739. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-540-92910-9_24
O’Neill, M., Ryan, C.: Grammatical evolution. Trans. Evol. Comput. 5(4), 349–358 (2001). https://doi.org/10.1109/4235.942529
Tettamanzi, A.G.B., Faron-Zucker, C., Gandon, F.: Possibilistic testing of OWL axioms against RDF data. Int. J. Approx. Reason. 91, 114–130 (2017)
Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)
De Luca, A., Termini, S.: A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Inf. Control 20, 301–312 (1972)
Acknowledgments
This work has been supported by the French government, through the 3IA Côte d’Azur “Investments in the Future” project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, T.H., Tettamanzi, A.G.B. (2020). Using Grammar-Based Genetic Programming for Mining Disjointness Axioms Involving Complex Class Expressions. In: Alam, M., Braun, T., Yun, B. (eds) Ontologies and Concepts in Mind and Machine. ICCS 2020. Lecture Notes in Computer Science(), vol 12277. Springer, Cham. https://doi.org/10.1007/978-3-030-57855-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-57855-8_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57854-1
Online ISBN: 978-3-030-57855-8
eBook Packages: Computer ScienceComputer Science (R0)