Abstract
In order to meet the explainability requirement of AI using Deep Learning (DL), this paper explores the contributions and feasibility of a process designed to create ontologically explainable classifiers while using domain ontologies. The approach is illustrated with the help of the Pizzas ontology that is used to create a synthetic image classifier that is able to provide visual explanations concerning a selection of ontological features. The approach is implemented by completing a DL model with ontological tensors that are generated from the ontology expressed in Description Logic.
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
References
Arrieta, A., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. ArXiv abs/1910.10045 (2020)
Bahadori, M.T., Heckerman, D.: Debiasing concept bottleneck models with a causal analysis technique (2020)
Confalonieri, R., Besold, T.R.: Trepan reloaded: a knowledge-driven approach to explaining black-box models. In: ECAI (2020)
Conigliaro, D., Ferrario, R., Hudelot, C., Porello, D.: Integrating computer vision algorithms and ontologies for spectator crowd behavior analysis. In: Group and Crowd Behavior for Computer Vision (2017)
Ding, Z., Yao, L., Liu, B., Wu, J.: Review of the application of ontology in the field of image object recognition. In: ICCMS 2019 (2019)
Dosilovic, F.K., Br\(\check{c}\)i\(\check{c}\), M., Hlupic, N.: Explainable artificial intelligence: a survey. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 0210–0215 (2018)
Gonzalez-Garcia, A., Modolo, D., Ferrari, V.: Do semantic parts emerge in convolutional neural networks? Int. J. Comput. Vis. 126, 476–494 (2017)
Hendricks, L.A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., Darrell, T.: Generating visual explanations. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part IV. LNCS, vol. 9908, pp. 3–19. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_1
Lipton, Z.C.: The mythos of model interpretability. Queue 16, 31–57 (2018)
Losch, M., Fritz, M., Schiele, B.: Interpretability beyond classification output: semantic bottleneck networks. ArXiv abs/1907.10882 (2019)
Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: NIPS (2017)
Marcos, D., Lobry, S., Tuia, D.: Semantically interpretable activation maps: what-where-how explanations within CNNs. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 4207–4215 (2019)
Martens, D., Provost, F.: Explaining data-driven document classifications. MIS Q. 38, 73–99 (2014)
Papadopoulos, D.P., Tamaazousti, Y., Ofli, F., Weber, I., Torralba, A.: How to make a pizza: learning a compositional layer-based GAN model. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7994–8003 (2019)
Ribeiro, M.T., Singh, S., Guestrin, C.: “why should i trust you?”: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. ArXiv abs/1505.04597 (2015)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Selvaraju, R.R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. 128, 336–359 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2015)
Zhang, Q., Wu, Y., Zhu, S.: Interpretable convolutional neural networks. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8827–8836 (2018)
Zhitomirsky-Geffet, M., Erez, E.S., Bar-Ilan, J.: Toward multiviewpoint ontology construction by collaboration of non-experts and crowdsourcing: the case of the effect of diet on health. J. Assoc. Inf. Sci. Technol. 68, 681–694 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Bourguin, G., Lewandowski, A., Bouneffa, M., Ahmad, A. (2021). Towards Ontologically Explainable Classifiers. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12892. Springer, Cham. https://doi.org/10.1007/978-3-030-86340-1_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-86340-1_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86339-5
Online ISBN: 978-3-030-86340-1
eBook Packages: Computer ScienceComputer Science (R0)