{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T22:28:39Z","timestamp":1767652119258,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031084201"},{"type":"electronic","value":"9783031084218"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-08421-8_37","type":"book-chapter","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T21:02:38Z","timestamp":1658178158000},"page":"532-549","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Generating Local Textual Explanations for\u00a0CNNs: A Semantic Approach Based on\u00a0Knowledge Graphs"],"prefix":"10.1007","author":[{"given":"Vitor A. C.","family":"Horta","sequence":"first","affiliation":[]},{"given":"Alessandra","family":"Mileo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"key":"37_CR1","doi-asserted-by":"publisher","unstructured":"Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82\u2013115 (2020). https:\/\/doi.org\/10.1016\/j.inffus.2019.12.012, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253519308103","DOI":"10.1016\/j.inffus.2019.12.012"},{"key":"37_CR2","doi-asserted-by":"crossref","unstructured":"Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: quantifying interpretability of deep visual representations. In: Computer Vision and Pattern Recognition (2017)","DOI":"10.1109\/CVPR.2017.354"},{"key":"37_CR3","doi-asserted-by":"publisher","unstructured":"Byrne, R.M.J.: Counterfactuals in explainable artificial intelligence (XAI): evidence from human reasoning. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19. International Joint Conferences on Artificial Intelligence Organization, pp. 6276\u20136282 (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/876","DOI":"10.24963\/ijcai.2019\/876"},{"key":"37_CR4","doi-asserted-by":"publisher","unstructured":"Chatzimparmpas, A., Martins, R.M., Jusufi, I., Kerren, A.: A survey of surveys on the use of visualization for interpreting machine learning models. Inf. Visual. 147387162090467 (2020). https:\/\/doi.org\/10.1177\/1473871620904671","DOI":"10.1177\/1473871620904671"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Cui, Y., Song, Y., Sun, C., Howard, A., Belongie, S.: Large scale fine-grained categorization and domain-specific transfer learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00432"},{"key":"37_CR6","doi-asserted-by":"crossref","unstructured":"Fong, R., Vedaldi, A.: Net2vec: quantifying and explaining how concepts are encoded by filters in deep neural networks (2018). http:\/\/arxiv.org\/abs\/1801.03454","DOI":"10.1109\/CVPR.2018.00910"},{"key":"37_CR7","doi-asserted-by":"publisher","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 11(2) (2020). https:\/\/doi.org\/10.3390\/info11020122","DOI":"10.3390\/info11020122"},{"key":"37_CR8","unstructured":"Garcia-Gasulla, D., et al.: On the behavior of convolutional nets for feature extraction (2017). http:\/\/arxiv.org\/abs\/1703.01127"},{"key":"37_CR9","doi-asserted-by":"publisher","unstructured":"Garcia-Gasulla, D., et al.: An out-of-the-box full-network embedding for convolutional neural networks. In: 2018 IEEE International Conference on Big Knowledge (ICBK), pp. 168\u2013175 (2018). https:\/\/doi.org\/10.1109\/ICBK.2018.00030","DOI":"10.1109\/ICBK.2018.00030"},{"key":"37_CR10","unstructured":"Gr\u00fcn, F., Rupprecht, C., Navab, N., Tombari, F.: A taxonomy and library for visualizing learned features in convolutional neural networks. arXiv preprint arXiv:1606.07757 (2016)"},{"key":"37_CR11","doi-asserted-by":"publisher","unstructured":"Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.Z.: XAI-explainable artificial intelligence. Sci. Robot. 4(37), eaay7120 (2019). https:\/\/doi.org\/10.1126\/scirobotics.aay7120, https:\/\/openaccess.city.ac.uk\/id\/eprint\/23405\/, this is the author\u2019s version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science Robotics 4(37) (2019). https:\/\/doi.org\/10.1126\/scirobotics.aay7120","DOI":"10.1126\/scirobotics.aay7120 10.1126\/scirobotics.aay7120"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Hendricks, L.A., Hu, R., Darrell, T., Akata, Z.: Grounding visual explanations (2018)","DOI":"10.1007\/978-3-030-01216-8_17"},{"key":"37_CR13","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/978-3-030-27684-3_20","volume-title":"Database and Expert Systems Applications","author":"VAC Horta","year":"2019","unstructured":"Horta, V.A.C., Mileo, A.: Towards explaining deep neural networks through graph analysis. In: Anderst-Kotsis, G., et al. (eds.) Database and Expert Systems Applications, pp. 155\u2013165. Springer International Publishing, Cham (2019)"},{"key":"37_CR14","doi-asserted-by":"publisher","unstructured":"Horta, V.A., Tiddi, I., Little, S., Mileo, A.: Extracting knowledge from deep neural networks through graph analysis. Future Gener. Comput. Syst. 120, 109\u2013118 (2021). https:\/\/doi.org\/10.1016\/j.future.2021.02.009, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X21000613","DOI":"10.1016\/j.future.2021.02.009"},{"key":"37_CR15","unstructured":"Kenny, E.M., Keane, M.T.: On generating plausible counterfactual and semi-factual explanations for deep learning. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, 2\u20139 February 2021, pp. 11575\u201311585. AAAI Press (2021). https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/17377"},{"issue":"1","key":"37_CR16","doi-asserted-by":"publisher","first-page":"41","DOI":"10.3233\/SW-190374","volume":"11","author":"F Lecue","year":"2019","unstructured":"Lecue, F.: On the role of knowledge graphs in explainable AI. Semant. Web 11(1), 41\u201351 (2019). https:\/\/doi.org\/10.3233\/SW-190374","journal-title":"Semant. Web"},{"key":"37_CR17","unstructured":"Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Tech. Rep. 1999\u201366, Stanford InfoLab, November 1999, http:\/\/ilpubs.stanford.edu:8090\/422\/previous number = SIDL-WP-1999-0120"},{"key":"37_CR18","doi-asserted-by":"crossref","unstructured":"Qin, Z., Yu, F., Liu, C., Chen, X.: How convolutional neural network see the world - a survey of convolutional neural network visualization methods (2018)","DOI":"10.3934\/mfc.2018008"},{"key":"37_CR19","doi-asserted-by":"publisher","unstructured":"Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Mach. Intell. 1(5), 206\u2013215 (2019). https:\/\/doi.org\/10.1038\/s42256-019-0048-x","DOI":"10.1038\/s42256-019-0048-x"},{"key":"37_CR20","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). http:\/\/arxiv.org\/abs\/1409.1556"},{"key":"37_CR21","unstructured":"Smyth, B., Keane, M.T.: A few good counterfactuals: generating interpretable, plausible and diverse counterfactual explanations (2021). https:\/\/arxiv.org\/abs\/2101.09056"},{"key":"37_CR22","doi-asserted-by":"publisher","unstructured":"Suzuki, M., Kamcya, Y., Kutsuna, T., Mitsumoto, N.: Understanding the reason for misclassification by generating counterfactual images. In: 2021 17th International Conference on Machine Vision and Applications (MVA), pp. 1\u20135 (2021). https:\/\/doi.org\/10.23919\/MVA51890.2021.9511352","DOI":"10.23919\/MVA51890.2021.9511352"},{"key":"37_CR23","unstructured":"Tjoa, E., Guan, C.: A survey on explainable artificial intelligence (XAI): towards medical XAI (2019). http:\/\/arxiv.org\/abs\/1907.07374"},{"key":"37_CR24","doi-asserted-by":"publisher","first-page":"420","DOI":"10.3389\/fnhum.2015.00420","volume":"9","author":"N Van Hoeck","year":"2015","unstructured":"Van Hoeck, N., Watson, P.D., Barbey, A.K.: Cognitive neuroscience of human counterfactual reasoning. Front. Hum. Neurosci. 9, 420 (2015). https:\/\/doi.org\/10.3389\/fnhum.2015.00420","journal-title":"Front. Hum. Neurosci."},{"key":"37_CR25","doi-asserted-by":"publisher","unstructured":"Vilone, G., Longo, L.: Classification of explainable artificial intelligence methods through their output formats. Mach. Learn. Knowl. Extr. 3(3), 615\u2013661 (2021). https:\/\/doi.org\/10.3390\/make3030032","DOI":"10.3390\/make3030032"},{"key":"37_CR26","unstructured":"Wan, A., et al.: NBDT: neural-backed decision trees (2020). https:\/\/arxiv.org\/abs\/2004.00221"},{"key":"37_CR27","unstructured":"Welinder, P., et al.: Caltech-UCSD birds 200. Technical report CNS-TR-2010-001, California Institute of Technology (2010)"}],"container-title":["Lecture Notes in Computer Science","AIxIA 2021 \u2013 Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-08421-8_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T21:08:40Z","timestamp":1658178520000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-08421-8_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031084201","9783031084218"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-08421-8_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIxIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference of the Italian Association for Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiia2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aixia2021.disco.unimib.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"58","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"62% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}