{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:16:35Z","timestamp":1761808595041,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031194320"},{"type":"electronic","value":"9783031194337"}],"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\/978-3-031-19433-7_5","type":"book-chapter","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T06:20:33Z","timestamp":1665901233000},"page":"74-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Towards Neural Network Interpretability Using Commonsense Knowledge Graphs"],"prefix":"10.1007","author":[{"given":"Youmna","family":"Ismaeil","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daria","family":"Stepanova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trung-Kien","family":"Tran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piyapat","family":"Saranrittichai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Csaba","family":"Domokos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hendrik","family":"Blockeel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,16]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: SIGMOD 1993, pp. 207\u2013216 (1993)","DOI":"10.1145\/170036.170072"},{"key":"5_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: CVPR, pp. 3319\u20133327 (2017)","DOI":"10.1109\/CVPR.2017.354"},{"key":"5_CR3","unstructured":"Brunner, T., Diehl, F., Knoll, A.: Copy and paste: a simple but effective initialization method for black-box adversarial attacks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2019)"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Chen, J., Geng, Y., Chen, Z., Horrocks, I., Pan, J.Z., Chen, H.: Knowledge-aware zero-shot learning: survey and perspective. In: IJCAI, pp. 4366\u20134373. ijcai.org (2021)","DOI":"10.24963\/ijcai.2021\/597"},{"key":"5_CR5","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/j.patcog.2017.09.025","volume":"74","author":"X Cheng","year":"2018","unstructured":"Cheng, X., Lu, J., Feng, J., Yuan, B., Zhou, J.: Scene recognition with objectness. Pattern Recogn. 74, 474\u2013487 (2018)","journal-title":"Pattern Recogn."},{"key":"5_CR6","doi-asserted-by":"crossref","unstructured":"Dalvi, F., Nortonsmith, A., Bau, A., et al.: Neurox: a toolkit for analyzing individual neurons in neural networks. In: AAAI 2019, pp. 9851\u20139852 (2019)","DOI":"10.1609\/aaai.v33i01.33019851"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., et al.: Imagenet: a large-scale hierarchical image database. In: CVPR 2009, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"5_CR8","unstructured":"Endres, D., F\u00f6ldi\u00e1k, P.: Interpreting the neural code with formal concept analysis. In: NIPS, pp. 425\u2013432 (2008)"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Fong, R., Vedaldi, A.: Net2vec: quantifying and explaining how concepts are encoded by filters in deep neural networks. In: CVPR, pp. 8730\u20138738 (2018)","DOI":"10.1109\/CVPR.2018.00910"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Geng, Y., Chen, J., Zhiquan Ye, e.: Explainable zero-shot learning via attentive graph convolutional network and KGs. SW 12 (2021)","DOI":"10.3233\/SW-210435"},{"key":"5_CR11","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: ICLR 2015 (2015)"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Horta, V.A.C., Mileo, A.: Towards explaining deep neural networks through graph analysis. In: DB and Expert Systems Applications, pp. 155\u2013165 (2019)","DOI":"10.1007\/978-3-030-27684-3_20"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Kampffmeyer, M., Chen, Y., Liang, X., Wang, H., Zhang, Y., Xing, E.P.: Rethinking knowledge graph propagation for zero-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.01175"},{"issue":"1","key":"5_CR15","doi-asserted-by":"publisher","first-page":"41","DOI":"10.3233\/SW-190374","volume":"11","author":"F L\u00e9cu\u00e9","year":"2020","unstructured":"L\u00e9cu\u00e9, F.: On the role of knowledge graphs in explainable AI. Semant. Web 11(1), 41\u201351 (2020)","journal-title":"Semant. Web"},{"key":"5_CR16","first-page":"17153","volume":"33","author":"J Mu","year":"2020","unstructured":"Mu, J., Andreas, J.: Compositional explanations of neurons. Adv. Neural Inf. Process. Syst. 33, 17153\u201317163 (2020)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"5_CR17","unstructured":"Nayak, N.V., Bach, S.H.: Zero-shot learning with common sense knowledge graphs. CoRR abs\/2006.10713 (2020)"},{"key":"5_CR18","unstructured":"Nguyen, A.M., Dosovitskiy, A., Jason Yosinski, e.: Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In: Neurips 2016, pp. 3387\u20133395 (2016)"},{"key":"5_CR19","doi-asserted-by":"publisher","unstructured":"Quattoni, A., Torralba, A.: Recognizing indoor scenes. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 413\u2013420 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206537","DOI":"10.1109\/CVPR.2009.5206537"},{"key":"5_CR20","unstructured":"Roy, A., Ghosal, D., Cambria, E., Majumder, N., Mihalcea, R., Poria, S.: Improving zero shot learning baselines with commonsense knowledge. CoRR abs\/2012.06236 (2020)"},{"key":"5_CR21","unstructured":"Sarker, M.K., Xie, N., Doran, D., Raymer, M., Hitzler, P.: Explaining trained neural networks with semantic web technologies: first steps. In: NeSy (2017)"},{"key":"5_CR22","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., et al.: Choose your neuron: incorporating domain knowledge through neuron-importance. In: ECCV (13), pp. 540\u2013556 (2018)","DOI":"10.1007\/978-3-030-01261-8_32"},{"key":"5_CR23","unstructured":"Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. In: ICLR 2014 (2014)"},{"key":"5_CR24","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR 2015 (2015)"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"de Sousa Ribeiro, M., Leite, J.: Aligning artificial neural networks and ontologies towards explainable AI. In: AAAI 2021, pp. 4932\u20134940 (2021)","DOI":"10.1609\/aaai.v35i6.16626"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Speer, R., Chin, J., Havasi, C.: Conceptnet 5.5: an open multilingual graph of general knowledge. In: AAAI 2017, pp. 4444\u20134451 (2017)","DOI":"10.1609\/aaai.v31i1.11164"},{"key":"5_CR27","doi-asserted-by":"crossref","unstructured":"Tandon, N., de Melo, G., Suchanek, F.M., Weikum, G.: Webchild: harvesting and organizing commonsense knowledge from the web. In: WSDM. ACM (2014)","DOI":"10.1145\/2556195.2556245"},{"issue":"9","key":"5_CR28","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1109\/TPAMI.2018.2857768","volume":"41","author":"Y Xian","year":"2019","unstructured":"Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning-a comprehensive evaluation. IEEE Trans. Pattern. Anal. Mach. Intell. 41(9), 2251\u20132265 (2019)","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."},{"key":"5_CR29","unstructured":"Zhou, B., Khosla, A., Lapedriza, \u00c0., Oliva, A., Torralba, A.: Object detectors emerge in deep scene cnns. In: ICLR 2015 (2015)"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web \u2013 ISWC 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19433-7_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T06:20:52Z","timestamp":1665901252000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19433-7_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031194320","9783031194337"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19433-7_5","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":"16 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"semweb2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iswc2022.semanticweb.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"239","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":"48","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":"20% - 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":"3.5","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":"3","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)"}}]}}