{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T19:02:16Z","timestamp":1791486136219,"version":"4.3.4"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032347411","type":"print"},{"value":"9783032347428","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T00:00:00Z","timestamp":1786406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-3-032-34742-8_24","type":"book-chapter","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T09:01:10Z","timestamp":1786352470000},"page":"351-361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Conceptual Views of\u00a0Neural Networks: A Framework for\u00a0Neuro-Symbolic Analysis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9034-0321","authenticated-orcid":false,"given":"Johannes","family":"Hirth","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4918-6374","authenticated-orcid":false,"given":"Tom","family":"Hanika","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,11]]},"reference":[{"key":"24_CR1","doi-asserted-by":"publisher","unstructured":"Bereska, L., Gavves, E.: Mechanistic interpretability for AI safety\u2014a review. Trans. Mach. Learn. Res. (2024). https:\/\/doi.org\/10.48550\/arxiv.2404.14082","DOI":"10.48550\/arxiv.2404.14082"},{"key":"24_CR2","unstructured":"B\u011blohl\u00e1vek, R., De\u00a0Baets, B., Outrata, J., Vychodil, V.: Inducing decision trees via concept lattices. In: Eklund, P.W., Diatta, J., Liquiere, M. (eds.) CLA. CEUR Workshop Proceedings, vol.\u00a0331, pp. 1\u201312. CEUR-WS.org (2007)"},{"key":"24_CR3","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: CVPR, pp. 248\u2013255. IEEE (2009). https:\/\/doi.org\/10.1109\/cvprw.2009.5206848","DOI":"10.1109\/cvprw.2009.5206848"},{"key":"24_CR4","doi-asserted-by":"publisher","unstructured":"Fong, R., Vedaldi, A.: Net2vec: quantifying and explaining how concepts are encoded by filters in deep neural networks. In: CVPR, pp. 8730\u20138738. IEEE (2018). https:\/\/doi.org\/10.1109\/cvpr.2018.00910","DOI":"10.1109\/cvpr.2018.00910"},{"key":"24_CR5","doi-asserted-by":"publisher","unstructured":"Ganter, B., Wille, R.: Conceptual scaling. In: Roberts, F. (ed.) Applications of Combinatorics and Graph Theory to the Biological and Social Sciences, pp. 139\u2013167. Springer (1989). https:\/\/doi.org\/10.1007\/978-1-4684-6381-1_6","DOI":"10.1007\/978-1-4684-6381-1_6"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Ganter, B., Wille, R.: Formal Concept Analysis - Mathematical Foundations, Second Edition. Springer (2024)","DOI":"10.1007\/978-3-031-63422-2"},{"key":"24_CR7","doi-asserted-by":"publisher","unstructured":"Hanika, T., Hirth, J.: On the lattice of conceptual measurements. Inf. Sci. 613, 453\u2013468 (2022). https:\/\/doi.org\/10.1016\/j.ins.2022.09.005","DOI":"10.1016\/j.ins.2022.09.005"},{"key":"24_CR8","doi-asserted-by":"publisher","unstructured":"Herrera, F., Carmona, C., Gonz\u00e1lez, P., del Jesus, M.: An overview on subgroup discovery: foundations and applications. Knowl. Inf. Syst. 29(3), 1\u201331 (2010). https:\/\/doi.org\/10.1007\/s10115-010-0356-2","DOI":"10.1007\/s10115-010-0356-2"},{"key":"24_CR9","unstructured":"Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., Sayres, R.: Interpretability beyond feature attribution: quantitative testing with concept activation vectors (TCAV). In: Dy, J.G., Krause, A. (eds.) ICML. Proceedings of Machine Learning Research, pp. 2668\u20132677. PMLR (2018)"},{"key":"24_CR10","doi-asserted-by":"publisher","unstructured":"Koh, P., et al.: Concept bottleneck models. In: ICML. Proceedings of Machine Learning Research, vol.\u00a0119, pp. 5338\u20135348. PMLR (2020). https:\/\/doi.org\/10.48550\/arxiv.2007.04612","DOI":"10.48550\/arxiv.2007.04612"},{"key":"24_CR11","doi-asserted-by":"publisher","unstructured":"Kornblith, S., Norouzi, M., Lee, H., Hinton, G.: Similarity of neural network representations revisited. In: Chaudhuri, K., Salakhutdinov, R. (eds.) ICML. Proceedings of Machine Learning Research, vol.\u00a097, pp. 3519\u20133529. PMLR (2019).https:\/\/doi.org\/10.48550\/arxiv.1905.00414","DOI":"10.48550\/arxiv.1905.00414"},{"key":"24_CR12","doi-asserted-by":"publisher","unstructured":"Mangal, R., Narodytska, N., Gopinath, D., Hu, B., Roy, A., Jha, S., Pasareanu, C.: Concept-based analysis of neural networks via vision-language models. In: ECAI, pp. 49\u201377. LNCS, Springer (2024). https:\/\/doi.org\/10.1007\/978-3-031-65112-0_3","DOI":"10.1007\/978-3-031-65112-0_3"},{"key":"24_CR13","doi-asserted-by":"publisher","unstructured":"M\u00e9moli, F.: Gromov\u2013wasserstein distances and the metric approach to object matching. Found. Comput. Math. 11(4), 417\u2013487 (2011). https:\/\/doi.org\/10.1007\/s10208-011-9093-5","DOI":"10.1007\/s10208-011-9093-5"},{"key":"24_CR14","unstructured":"Mure\u015fan, H., Oltean, M.: Fruit recognition from images using deep learning. arXiv preprint arXiv:1712.00580 (2017). https:\/\/arxiv.org\/pdf\/1712.00580"},{"key":"24_CR15","doi-asserted-by":"publisher","unstructured":"Ribeiro, M., Singh, S., Guestrin, C.: \u201cwhy should I trust you?\u201d: explaining the predictions of any classifier. In: Krishnapuram, B., Shah, M., Smola, A.J., Aggarwal, C., Shen, D., Rastogi, R. (eds.) KDD. pp. 1135\u20131144. ACM (2016). https:\/\/doi.org\/10.48550\/arxiv.1602.04938","DOI":"10.48550\/arxiv.1602.04938"},{"key":"24_CR16","doi-asserted-by":"publisher","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206\u2013215 (2019). https:\/\/doi.org\/10.1038\/s42256-019-0048-x","DOI":"10.1038\/s42256-019-0048-x"},{"key":"24_CR17","doi-asserted-by":"publisher","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","DOI":"10.1007\/s11263-015-0816-y"},{"key":"24_CR18","doi-asserted-by":"publisher","unstructured":"Sarker, M., Zhou, L., Eberhart, A., Hitzler, P.: Neuro-symbolic artificial intelligence. AI Commun. 34(3), 197\u2013209 (2022).https:\/\/doi.org\/10.3233\/aic-210084","DOI":"10.3233\/aic-210084"}],"container-title":["Lecture Notes in Computer Science","Conceptual Knowledge Structures"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-34742-8_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T17:59:23Z","timestamp":1791482363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-34742-8_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,11]]},"ISBN":["9783032347411","9783032347428"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-34742-8_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,11]]},"assertion":[{"value":"11 August 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CONCEPTS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Conference on Conceptual Knowledge Structures","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montpellier","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 August 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-conceptstruct2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}