{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T23:15:03Z","timestamp":1743030903732,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031606052"},{"type":"electronic","value":"9783031606069"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-60606-9_15","type":"book-chapter","created":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:06:47Z","timestamp":1717204007000},"page":"264-276","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Ontology-Based Explanations of\u00a0Neural Networks: A User Perspective"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9380-5064","authenticated-orcid":false,"given":"Andrew","family":"Ponomarev","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7960-8929","authenticated-orcid":false,"given":"Anton","family":"Agafonov","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,1]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","unstructured":"Agafonov, A., Ponomarev, A.: RevelioNN: retrospective extraction of visual and logical insights for ontology-based interpretation of neural networks. In: 2023 34th Conference of Open Innovations Association (FRUCT), pp.\u00a03\u20139. IEEE, November 2023. https:\/\/doi.org\/10.23919\/FRUCT60429.2023.10328156, https:\/\/ieeexplore.ieee.org\/document\/10328156\/","DOI":"10.23919\/FRUCT60429.2023.10328156"},{"key":"15_CR2","unstructured":"Bellucci, M., Delestre, N., Malandain, N., Zanni-merk, C.: Ontologies to build a predictive architecture to classify and explain. In: DeepOntoNLP Workshop @ESWC 2022 (2022). https:\/\/hal.archives-ouvertes.fr\/hal-03684275"},{"key":"15_CR3","doi-asserted-by":"publisher","unstructured":"Bourgeais, V., Zehraoui, F., Ben Hamdoune, M., Hanczar, B.: Deep GONet: self-explainable deep neural network based on Gene Ontology for phenotype prediction from gene expression data. BMC Bioinform. 22, 1\u201324 (2021). https:\/\/doi.org\/10.1186\/s12859-021-04370-7, https:\/\/doi.org\/10.1186\/s12859-021-04370-7","DOI":"10.1186\/s12859-021-04370-7"},{"key":"15_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1007\/978-3-030-86340-1_38","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2021","author":"G Bourguin","year":"2021","unstructured":"Bourguin, G., Lewandowski, A., Bouneffa, M., Ahmad, A.: Towards ontologically explainable classifiers. In: Farka\u0161, I., Masulli, P., Otte, S., Wermter, S. (eds.) ICANN 2021. LNCS, vol. 12892, pp. 472\u2013484. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86340-1_38"},{"key":"15_CR5","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1613\/JAIR.1.12228","volume":"70","author":"N Burkart","year":"2021","unstructured":"Burkart, N., Huber, M.F.: A survey on the explainability of supervised machine learning. J. Artif. Intell. Res. 70, 245\u2013317 (2021). https:\/\/doi.org\/10.1613\/JAIR.1.12228","journal-title":"J. Artif. Intell. Res."},{"key":"15_CR6","doi-asserted-by":"publisher","first-page":"2457","DOI":"10.3233\/FAIA200378","volume":"325","author":"R Confalonieri","year":"2020","unstructured":"Confalonieri, R., Weyde, T., Besold, T.R., Del Prado, M., Mart\u00edn, F.: Trepan reloaded: a knowledge-driven approach to explaining black-box models. Front. Artif. Intell. Appl. 325, 2457\u20132464 (2020). https:\/\/doi.org\/10.3233\/FAIA200378","journal-title":"Front. Artif. Intell. Appl."},{"key":"15_CR7","doi-asserted-by":"publisher","unstructured":"Confalonieri, R., Weyde, T., Besold, T.R., Moscoso del Prado Mart\u00edn, F.: Using ontologies to enhance human understandability of global post-hoc explanations of black-box models. Artif. Intell. 296, 103471 (2021). https:\/\/doi.org\/10.1016\/j.artint.2021.103471","DOI":"10.1016\/j.artint.2021.103471"},{"key":"15_CR8","doi-asserted-by":"publisher","unstructured":"Daniels, Z.A., Frank, L.D., Menart, C., Raymer, M., Hitzler, P.: A framework for explainable deep neural models using external knowledge graphs. In: Pham, T., Solomon, L., Rainey, K. (eds.) Proceedings of SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, p.\u00a073. SPIE, April 2020. https:\/\/doi.org\/10.1117\/12.2558083, https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/11413\/2558083\/A-framework-for-explainable-deep-neural-models-using-external-knowledge\/10.1117\/12.2558083.full","DOI":"10.1117\/12.2558083"},{"key":"15_CR9","unstructured":"de Sousa Ribeiro, M., Krippahl, L., Leite, J.: Explainable Abstract Trains Dataset, December 2020. http:\/\/arxiv.org\/abs\/2012.12115"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"de Sousa Ribeiro, M., Leite, J.: Aligning artificial neural networks and ontologies towards explainable AI. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 4932\u20134940 (2021). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16626","DOI":"10.1609\/aaai.v35i6.16626"},{"key":"15_CR11","unstructured":"Doshi-Velez, F., Kim, B.: Towards A Rigorous Science of Interpretable Machine Learning, February 2017. http:\/\/arxiv.org\/abs\/1702.08608"},{"issue":"2","key":"15_CR12","doi-asserted-by":"publisher","first-page":"122","DOI":"10.3390\/info11020122","volume":"11","author":"G Futia","year":"2020","unstructured":"Futia, G., Vetr\u00f2, A.: On the integration of knowledge graphs into deep learning models for a more comprehensible AI-Three challenges for future research. Information (Switzerland) 11(2), 122 (2020). https:\/\/doi.org\/10.3390\/info11020122","journal-title":"Information (Switzerland)"},{"key":"15_CR13","unstructured":"Hoffman, R.R., Mueller, S.T., Klein, G., Litman, J.: Metrics for Explainable AI: Challenges and Prospects (2018). http:\/\/arxiv.org\/abs\/1812.04608"},{"key":"15_CR14","doi-asserted-by":"publisher","unstructured":"Huysmans, J., Dejaeger, K., Mues, C., Vanthienen, J., Baesens, B.: An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models. Decis. Support Syst. 51(1), 141\u2013154 (2011). https:\/\/doi.org\/10.1016\/j.dss.2010.12.003, https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167923610002368","DOI":"10.1016\/j.dss.2010.12.003"},{"key":"15_CR15","doi-asserted-by":"publisher","unstructured":"Liao, Q.V., Gruen, D., Miller, S.: Questioning the AI: informing design practices for explainable AI user experiences. In: Proceedings of Conference on Human Factors in Computing Systems, pp. 1\u201315 (2020). https:\/\/doi.org\/10.1145\/3313831.3376590","DOI":"10.1145\/3313831.3376590"},{"key":"15_CR16","doi-asserted-by":"publisher","unstructured":"Lipton, Z.C.: The Mythos of Model Interpretability. Queue 16(3), 31\u201357 (2018). https:\/\/doi.org\/10.1145\/3236386.3241340, https:\/\/dl.acm.org\/doi\/10.1145\/3236386.3241340","DOI":"10.1145\/3236386.3241340"},{"key":"15_CR17","unstructured":"Martin, T., Diallo, A.B., Valtchev, P., Lacroix, R.: Bridging the gap between an ontology and deep neural models by pattern mining. In: The Joint Ontology Workshops, JOWO 2020, CEUR vol. 2708 (2020). http:\/\/ceur-ws.org\/Vol-2708\/donlp4.pdf"},{"key":"15_CR18","doi-asserted-by":"publisher","unstructured":"Mucha, H., Robert, S., Breitschwerdt, R., Fellmann, M.: Interfaces for explanations in human-AI interaction: proposing a design evaluation approach. In: Proceedings of Conference on Human Factors in Computing Systems (2021). https:\/\/doi.org\/10.1145\/3411763.3451759","DOI":"10.1145\/3411763.3451759"},{"key":"15_CR19","doi-asserted-by":"publisher","unstructured":"Panigutti, C., Perotti, A., Pedreschi, D.: Doctor XAI: an ontology-based approach to black-box sequential data classification explanations. In: FAT* 2020 - Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 629\u2013639 (2020). https:\/\/doi.org\/10.1145\/3351095.3372855","DOI":"10.1145\/3351095.3372855"},{"key":"15_CR20","doi-asserted-by":"publisher","unstructured":"Ponomarev, A., Agafonov, A.: Ontology concept extraction algorithm for deep neural networks. In: 2022 32nd Conference of Open Innovations Association (FRUCT), pp. 221\u2013226. IEEE, November 2022. https:\/\/doi.org\/10.23919\/FRUCT56874.2022.9953838, https:\/\/ieeexplore.ieee.org\/document\/9953838\/","DOI":"10.23919\/FRUCT56874.2022.9953838"},{"key":"15_CR21","unstructured":"Poursabzi-Sangdeh, F., Goldstein, D.G., Hofman, J.M., Vaughan, J.W., Wallach, H.: Manipulating and Measuring Model Interpretability, February 2018. http:\/\/arxiv.org\/abs\/1802.07810"},{"key":"15_CR22","doi-asserted-by":"publisher","unstructured":"Ribeiro, M., Singh, S., Guestrin, C.: \u201cWhy Should I Trust You?\u201d: explaining the predictions of any classifier. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pp. 97\u2013101. Association for Computational Linguistics, Stroudsburg, PA, USA (2016). https:\/\/doi.org\/10.18653\/v1\/N16-3020, http:\/\/aclweb.org\/anthology\/N16-3020","DOI":"10.18653\/v1\/N16-3020"},{"key":"15_CR23","unstructured":"Slack, D., Friedler, S.A., Scheidegger, C., Roy, C.D.: Assessing the Local Interpretability of Machine Learning Models, February 2019. http:\/\/arxiv.org\/abs\/1902.03501"},{"key":"15_CR24","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/978-3-030-29726-8_6","volume-title":"Machine Learning and Knowledge Extraction","author":"J Voogd","year":"2019","unstructured":"Voogd, J., de Heer, P., Veltman, K., Hanckmann, P., van Lith, J.: Using relational concept networks for explainable decision support. In: Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-MAKE 2019. LNCS, vol. 11713, pp. 78\u201393. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29726-8_6"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in HCI"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-60606-9_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T01:07:17Z","timestamp":1717204037000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-60606-9_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031606052","9783031606069"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-60606-9_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"1 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HCII","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Human-Computer Interaction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Washington DC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hcii2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.hci.international\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}