Abstract
Computing the degree of concept similarity is an essential problem in description logic ontologies as it has contributions in various applications. However, many computational approaches to concept similarity do not take into account the logical relationships defined in an ontology. Moreover, they cannot be personalized to subjective factors (i.e. the agent’s preferences). This work introduces a computational approach to concept similarity for the description logic \(\mathcal {A}\!\mathcal {L}\!\mathcal {E}\!\mathcal {H}\). Our approach computes the degree of similarity between two concept descriptions structurally under the agent’s preferences. Hence, the derived degree is analyzed based on the logical definitions defined in an ontology. We also illustrate its applicability in rice disease detection, in which a farmer queries for relevant disease based on an agricultural observation.
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Notes
- 1.
For the sake of cleanliness, we simply write \(\mu ^e\) and \(\mu ^a\) for \(\mu ^e (\mathcal {P} _D, \mathcal {E} _D, \mathcal {A} _D)\) and \(\mu ^a (\mathcal {P} _D, \mathcal {E} _D, \mathcal {A} _D)\), respectively, in Eq. 4.
- 2.
Obvious abbreviations may be used for succinctness e.g. \(\mathsf {S}\) stands for \(\mathsf {Spot}\).
References
Ashburner, M., et al.: Gene ontology: tool for the unification of biology. Nat. Genet. 25(1), 25–29 (2000). https://doi.org/10.1038/75556
Baader, F., Küsters, R.: Nonstandard inferences in description logics: the story so far. In: Gabbay, D., Goncharov, S., Zakharyaschev, M. (eds.) Mathematical Problems from Applied Logic I, International Mathematical Series, vol. 4, pp. 1–75. Springer, New York (2006). https://doi.org/10.1007/0-387-31072-X_1
Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F.: The Description Logic Handbook: Theory, Implementation and Applications, 2nd edn. Cambridge University Press, New York (2010)
Bille, P.: A survey on tree edit distance and related problems. Theor. Comput. Sci. 337(1–3), 217–239 (2005)
Ge, J., Qiu, Y.: Concept similarity matching based on semantic distance. In: Proceedings of the 4th International Conference on Semantics, Knowledge and Grid. pp. 380–383, December 2008. https://doi.org/10.1109/SKG.2008.24
Hesse, M.B.: Models and Analogies in Science (1965)
Jearanaiwongkul, W., Anutariya, C., Andres, F.: An ontology-based approach to plant disease identification system (accepted). New Generation Computing (2019)
Lehmann, K., Turhan, A.Y.: A framework for semantic-based similarity measures for \(\cal{E}\!\cal{L}\!\cal{H}\) -concepts. In: del Cerro, L.F., Herzig, A., Mengin, J. (eds.) Logics in Artificial Intelligence, pp. 307–319. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-33353-8_24
Racharak, T., Suntisrivaraporn, B., Tojo, S.: Personalizing a concept similarity measure in the description logic \(\cal{E}\!\cal{L}\!\cal{H}\) with preference profile. Comput. Inform. 37(3), 581–613 (2018)
Racharak, T., Tojo, S.: Concept similarity under the agent’s preferences for the description logic \(\cal{F}\!\!\cal{L}_0\) with unfoldable TBox. In: Proceedings of the 10th International Conference on Agents and Artificial Intelligence, ICAART 2018, Funchal, Madeira, Portugal, 16–18 January 2018, vol. 2, pp. 201–210 (2018). https://doi.org/10.5220/0006653402010210
Racharak, T., Tojo, S., Hung, N.D., Boonkwan, P.: Combining answer set programming with description logics for analogical reasoning under an agent’s preferences. In: Benferhat, S., Tabia, K., Ali, M. (eds.) IEA/AIE 2017. LNCS (LNAI), vol. 10351, pp. 306–316. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60045-1_33
Suntisrivaraporn., B., Tongphu., S.: A structural subsumption based similarity measure for the description logic \(\cal{A}\!\cal{L}\!\cal{E}\!\cal{H}\). In: Proceedings of the 8th International Conference on Agents and Artificial Intelligence -ICAART, vol. 2, pp. 204–212. INSTICC, SciTePress (2016). https://doi.org/10.5220/0005819302040212
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Racharak, T., Jearanaiwongkul, W., Anutariya, C. (2020). Concept Similarity Under the Agent’s Preferences for the Description Logic \(\mathcal {A}\!\mathcal {L}\!\mathcal {E}\!\mathcal {H}\). In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Communications in Computer and Information Science, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-3412-6_4
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