{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T19:39:25Z","timestamp":1771616365897,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":75,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,10]]},"DOI":"10.1145\/3514221.3522564","type":"proceedings-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T02:33:49Z","timestamp":1655001229000},"page":"2452-2457","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Explainable AI: Foundations, Applications, Opportunities for Data Management Research"],"prefix":"10.1145","author":[{"given":"Romila","family":"Pradhan","sequence":"first","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]},{"given":"Aditya","family":"Lahiri","sequence":"additional","affiliation":[{"name":"University of California, San Diego, La Jolla, CA, USA"}]},{"given":"Sainyam","family":"Galhotra","sequence":"additional","affiliation":[{"name":"University of Chicago, Chicago, IL, USA"}]},{"given":"Babak","family":"Salimi","sequence":"additional","affiliation":[{"name":"University of California, San Diego, La Jolla, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,6,11]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Sanity Checks for Saliency Maps. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018","author":"Adebayo Julius","year":"2018","unstructured":"Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, and Been Kim. 2018. Sanity Checks for Saliency Maps. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3--8, 2018, Montr\u00e9al, Canada, Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicol\u00f2 Cesa-Bianchi, and Roman Garnett (Eds.). 9525--9536. https:\/\/proceedings.neurips.cc\/paper\/2018\/hash\/294a8ed24b1ad22ec2e7efea049b8737-Abstract.html"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/170035.170072"},{"key":"e_1_3_2_1_3_1","unstructured":"Rakesh Agrawal Ramakrishnan Srikant et al. 1994. Fast algorithms for mining association rules. PVLDB."},{"key":"e_1_3_2_1_4_1","volume-title":"Counterfactual Shapley Additive Explanations. arXiv preprint arXiv:2110.14270","author":"Albini Emanuele","year":"2021","unstructured":"Emanuele Albini, Jason Long, Danial Dervovic, and Daniele Magazzeni. 2021. Counterfactual Shapley Additive Explanations. arXiv preprint arXiv:2110.14270 (2021)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Marcelo Arenas Pablo Barcel\u00f3 Leopoldo E. Bertossi and Mika\u00ebl Monet. 2021. The Tractability of SHAP-Score-Based Explanations for Classification over Deterministic and Decomposable Boolean Circuits. In AAAI. 6670--6678. https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/16825","DOI":"10.1609\/aaai.v35i8.16825"},{"key":"e_1_3_2_1_6_1","volume-title":"PVLDB","volume":"11","author":"Bach Alexander Ratner","year":"2017","unstructured":"Alexander Ratner Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher R\u00e9. 2017. Snorkel: Rapid Training Data Creation with Weak Supervision. PVLDB, Vol. 11, 3 (2017)."},{"key":"e_1_3_2_1_7_1","volume-title":"On Second-Order Group Influence Functions for Black-Box Predictions. In International Conference on Machine Learning. PMLR, 715--724","author":"Basu Samyadeep","year":"2020","unstructured":"Samyadeep Basu, Xuchen You, and Soheil Feizi. 2020. On Second-Order Group Influence Functions for Black-Box Predictions. In International Conference on Machine Learning. PMLR, 715--724."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3173574.3173951"},{"key":"e_1_3_2_1_10_1","volume-title":"Cook and Sanford Weisberg","author":"Dennis","year":"1980","unstructured":"Dennis R. Cook and Sanford Weisberg. 1980. Characterizations of an Empirical Influence Function for Detecting Influential Cases in Regression. Technometrics, Vol. 22 (1980)."},{"key":"e_1_3_2_1_11_1","volume-title":"On The Reasons Behind Decisions. arXiv preprint arXiv:2002.09284","author":"Darwiche Adnan","year":"2020","unstructured":"Adnan Darwiche and Auguste Hirth. 2020. On The Reasons Behind Decisions. arXiv preprint arXiv:2002.09284 (2020)."},{"key":"e_1_3_2_1_12_1","unstructured":"Adnan Darwiche and Judea Pearl. 1994. Symbolic causal networks. In AAAI. 238--244."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2016.42"},{"key":"e_1_3_2_1_14_1","volume-title":"Characterizing diagnoses and systems. Artificial intelligence","author":"Kleer Johan De","year":"1992","unstructured":"Johan De Kleer, Alan K Mackworth, and Raymond Reiter. 1992. Characterizing diagnoses and systems. Artificial intelligence, Vol. 56, 2--3 (1992), 197--222."},{"key":"e_1_3_2_1_15_1","volume-title":"A guide to the california consumer privacy act of","author":"de la Torre Lydia","year":"2018","unstructured":"Lydia de la Torre. 2018. A guide to the california consumer privacy act of 2018. Available at SSRN 3275571 (2018)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482341"},{"key":"e_1_3_2_1_17_1","volume-title":"Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability. arXiv preprint arXiv:1910.06358","author":"Frye Christopher","year":"2019","unstructured":"Christopher Frye, Colin Rowat, and Ilya Feige. 2019. Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability. arXiv preprint arXiv:1910.06358 (2019)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457334"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3458455"},{"key":"e_1_3_2_1_20_1","unstructured":"Sainyam Galhotra Romila Pradhan and Babak Salimi. 2021 c. Feature Attribution and Recourse via Probabilistic Contrastive Counterfactuals."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013681"},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning","volume":"119","author":"Ghorbani Amirata","year":"2020","unstructured":"Amirata Ghorbani, Michael Kim, and James Zou. 2020. A Distributional Framework For Data Valuation. In Proceedings of the 37th International Conference on Machine Learning, Vol. 119. 3535--3544. http:\/\/proceedings.mlr.press\/v119\/ghorbani20a.html"},{"key":"e_1_3_2_1_23_1","volume-title":"Data Shapley: Equitable Valuation of Data for Machine Learning. In Proceedings of the 36th International Conference on Machine Learning","volume":"97","author":"Ghorbani Amirata","unstructured":"Amirata Ghorbani and James Y. Zou. 2019. Data Shapley: Equitable Valuation of Data for Machine Learning. In Proceedings of the 36th International Conference on Machine Learning, Vol. 97. 2242--2251."},{"key":"e_1_3_2_1_24_1","volume-title":"Causes and explanations: A structural-model approach. Part II: Explanations. The British journal for the philosophy of science","author":"Halpern Joseph Y","year":"2005","unstructured":"Joseph Y Halpern and Judea Pearl. 2005. Causes and explanations: A structural-model approach. Part II: Explanations. The British journal for the philosophy of science, Vol. 56, 4 (2005), 889--911."},{"key":"e_1_3_2_1_25_1","volume-title":"Data mining: concepts and techniques","author":"Han Jiawei","unstructured":"Jiawei Han, Jian Pei, and Micheline Kamber. 2011. Data mining: concepts and techniques .Elsevier."},{"key":"e_1_3_2_1_26_1","volume-title":"Mining frequent patterns without candidate generation. ACM sigmod record","author":"Han Jiawei","year":"2000","unstructured":"Jiawei Han, Jian Pei, and Yiwen Yin. 2000. Mining frequent patterns without candidate generation. ACM sigmod record, Vol. 29, 2 (2000), 1--12."},{"key":"e_1_3_2_1_27_1","volume-title":"A vS cedrov, and Stephen G Simpson","author":"Harrington Leo A","year":"1985","unstructured":"Leo A Harrington, Michael D Morley, A vS cedrov, and Stephen G Simpson. 1985. Harvey Friedman's research on the foundations of mathematics .Elsevier."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-017-0486--1"},{"key":"e_1_3_2_1_29_1","volume-title":"Ioan Gabriel Bucur, and Tom Claassen","author":"Heskes Tom","year":"2020","unstructured":"Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, and Tom Claassen. 2020. Causal shapley values: Exploiting causal knowledge to explain individual predictions of complex models. arXiv preprint arXiv:2011.01625 (2020)."},{"key":"e_1_3_2_1_30_1","volume-title":"Proceedings of the AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning. AAAI Press Menlo Park, CA, 83--89","author":"Hopkins Mark","year":"2003","unstructured":"Mark Hopkins and Judea Pearl. 2003. Clarifying the usage of structural models for commonsense causal reasoning. In Proceedings of the AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning. AAAI Press Menlo Park, CA, 83--89."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Alexey Ignatiev. 2020. Towards Trustable Explainable AI.. In IJCAI. 5154--5158.","DOI":"10.24963\/ijcai.2020\/726"},{"key":"e_1_3_2_1_32_1","volume-title":"Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness? arXiv preprint arXiv:2004.03685","author":"Jacovi Alon","year":"2020","unstructured":"Alon Jacovi and Yoav Goldberg. 2020. Towards faithfully interpretable NLP systems: How should we define and evaluate faithfulness? arXiv preprint arXiv:2004.03685 (2020)."},{"key":"e_1_3_2_1_33_1","volume-title":"Towards Efficient Data Valuation Based on the Shapley Value. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research","volume":"1176","author":"Jia Ruoxi","unstructured":"Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve G\u00fcrel, Bo Li, Ce Zhang, Dawn Song, and Costas J. Spanos. 2019. Towards Efficient Data Valuation Based on the Shapley Value. In Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 89). PMLR, 1167--1176. https:\/\/proceedings.mlr.press\/v89\/jia19a.html"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445899"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445866"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2702123.2702520"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1080\/1369118X.2018.1477967"},{"key":"e_1_3_2_1_38_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Koh Pang Wei","year":"2017","unstructured":"Pang Wei Koh and Percy Liang. 2017. Understanding black-box predictions via influence functions. In International Conference on Machine Learning. PMLR, 1885--1894."},{"key":"e_1_3_2_1_39_1","volume-title":"International Conference on Machine Learning. PMLR, 5491--5500","author":"Kumar I Elizabeth","year":"2020","unstructured":"I Elizabeth Kumar, Suresh Venkatasubramanian, Carlos Scheidegger, and Sorelle Friedler. 2020. Problems with Shapley-value-based explanations as feature importance measures. In International Conference on Machine Learning. PMLR, 5491--5500."},{"key":"e_1_3_2_1_40_1","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics. 793--801","author":"Kwon Yongchan","year":"2021","unstructured":"Yongchan Kwon, Manuel A. Rivas, and James Zou. 2021. Efficient Computation and Analysis of Distributional Shapley Values. In Proceedings of the International Conference on Artificial Intelligence and Statistics. 793--801. http:\/\/proceedings.mlr.press\/v130\/kwon21a.html"},{"key":"e_1_3_2_1_41_1","volume-title":"Accurate and Intuitive Contextual Explanations using Linear Model Trees. arXiv preprint arXiv:2009.05322","author":"Lahiri Aditya","year":"2020","unstructured":"Aditya Lahiri and Narayanan Unny Edakunni. 2020. Accurate and Intuitive Contextual Explanations using Linear Model Trees. arXiv preprint arXiv:2009.05322 (2020)."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939874"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13347-017-0279-x"},{"key":"e_1_3_2_1_44_1","volume-title":"Ifs","author":"Lewis David","unstructured":"David Lewis. 1973. Counterfactuals and comparative possibility. In Ifs. Springer, 57--85."},{"key":"e_1_3_2_1_45_1","volume-title":"From local explanations to global understanding with explainable AI for trees. Nature machine intelligence","author":"Lundberg Scott M","year":"2020","unstructured":"Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee. 2020. From local explanations to global understanding with explainable AI for trees. Nature machine intelligence, Vol. 2, 1 (2020), 56--67."},{"key":"e_1_3_2_1_46_1","volume-title":"Proceedings of the 31st international conference on neural information processing systems. 4768--4777","author":"Lundberg Scott M","year":"2017","unstructured":"Scott M Lundberg and Su-In Lee. 2017. A unified approach to interpreting model predictions. In Proceedings of the 31st international conference on neural information processing systems. 4768--4777."},{"key":"e_1_3_2_1_47_1","volume-title":"Preserving causal constraints in counterfactual explanations for machine learning classifiers. arXiv preprint arXiv:1912.03277","author":"Mahajan Divyat","year":"2019","unstructured":"Divyat Mahajan, Chenhao Tan, and Amit Sharma. 2019. Preserving causal constraints in counterfactual explanations for machine learning classifiers. arXiv preprint arXiv:1912.03277 (2019)."},{"key":"e_1_3_2_1_48_1","volume-title":"Proceedings of the Fourth International VLDB workshop on Management of Uncertain Data (MUD 2010) in conjunction with VLDB 2010, Singapore, September 13, 2010 (CTIT Workshop Proceedings Series","volume":"04","author":"Meliou Alexandra","year":"2010","unstructured":"Alexandra Meliou, Wolfgang Gatterbauer, Katherine F. Moore, and Dan Suciu. 2010. WHY SO? or WHY NO? Functional Causality for Explaining Query Answers. In Proceedings of the Fourth International VLDB workshop on Management of Uncertain Data (MUD 2010) in conjunction with VLDB 2010, Singapore, September 13, 2010 (CTIT Workshop Proceedings Series, Vol. WP10-04 ), Ander de Keijzer and Maurice van Keulen (Eds.). Centre for Telematics and Information Technology (CTIT), University of Twente, The Netherlands, 3--17. http:\/\/ewi1276.ewi.utwente.nl:3000\/papers\/MUD2010_whyso.pdf"},{"key":"e_1_3_2_1_49_1","unstructured":"Christoph Molnar. 2020. Interpretable Machine Learning .Lulu. com."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372850"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397461"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939778"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2588578"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.14778\/3229863.3236260"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389759"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3319901"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457239"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.14778\/3461535.3461555"},{"key":"e_1_3_2_1_60_1","volume-title":"Cxplain: Causal explanations for model interpretation under uncertainty. arXiv preprint arXiv:1910.12336","author":"Schwab Patrick","year":"2019","unstructured":"Patrick Schwab and Walter Karlen. 2019. Cxplain: Causal explanations for model interpretation under uncertainty. arXiv preprint arXiv:1910.12336 (2019)."},{"key":"e_1_3_2_1_61_1","volume-title":"Logical Methods in Computer Science","volume":"17","author":"Sebag Moshe","year":"2021","unstructured":"Moshe Sebag, Benny Kimelfeld, Leopoldo Bertossi, and Ester Livshits. 2021. The Shapley Value of Tuples in Query Answering. Logical Methods in Computer Science, Vol. 17 (2021)."},{"key":"e_1_3_2_1_62_1","volume-title":"A value for n-person games","author":"Shapley Lloyd S","unstructured":"Lloyd S Shapley. 1953. A value for n-person games .Princeton University Press."},{"key":"e_1_3_2_1_63_1","unstructured":"Boris Sharchilev Yury Ustinovsky Pavel Serdyukov and M. de Rijke. 2018. Finding Influential Training Samples for Gradient Boosted Decision Trees. In ICML ."},{"key":"e_1_3_2_1_64_1","volume-title":"A symbolic approach to explaining bayesian network classifiers. arXiv preprint arXiv:1805.03364","author":"Shih Andy","year":"2018","unstructured":"Andy Shih, Arthur Choi, and Adnan Darwiche. 2018. A symbolic approach to explaining bayesian network classifiers. arXiv preprint arXiv:1805.03364 (2018)."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375830"},{"key":"e_1_3_2_1_66_1","volume-title":"arXiv preprint arXiv:2106.02666","author":"Slack Dylan","year":"2021","unstructured":"Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju, and Sameer Singh. 2021. Counterfactual Explanations Can Be Manipulated. arXiv preprint arXiv:2106.02666 (2021)."},{"key":"e_1_3_2_1_67_1","volume-title":"bLIMEy: surrogate prediction explanations beyond LIME. arXiv preprint arXiv:1910.13016","author":"Sokol Kacper","year":"2019","unstructured":"Kacper Sokol, Alexander Hepburn, Raul Santos-Rodriguez, and Peter Flach. 2019. bLIMEy: surrogate prediction explanations beyond LIME. arXiv preprint arXiv:1910.13016 (2019)."},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287566"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"crossref","unstructured":"Guy Van den Broeck Anton Lykov Maximilian Schleich and Dan Suciu. 2021. On the tractability of SHAP explanations. In AAAI .","DOI":"10.1609\/aaai.v35i7.16806"},{"key":"e_1_3_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.14778\/3291264.3291268"},{"key":"e_1_3_2_1_71_1","volume-title":"Explainable Image Classification with Evidence Counterfactual. arXiv preprint arXiv:2004.07511","author":"Vermeire Tom","year":"2020","unstructured":"Tom Vermeire and David Martens. 2020. Explainable Image Classification with Evidence Counterfactual. arXiv preprint arXiv:2004.07511 (2020)."},{"key":"e_1_3_2_1_72_1","volume-title":"Statistical stability indices for LIME: obtaining reliable explanations for machine learning models. Journal of the Operational Research Society","author":"Visani Giorgio","year":"2020","unstructured":"Giorgio Visani, Enrico Bagli, Federico Chesani, Alessandro Poluzzi, and Davide Capuzzo. 2020. Statistical stability indices for LIME: obtaining reliable explanations for machine learning models. Journal of the Operational Research Society (2020), 1--11."},{"key":"e_1_3_2_1_73_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 721--729","author":"Wang Jiaxuan","year":"2021","unstructured":"Jiaxuan Wang, Jenna Wiens, and Scott Lundberg. 2021. Shapley flow: A graph-based approach to interpreting model predictions. In International Conference on Artificial Intelligence and Statistics. PMLR, 721--729."},{"key":"e_1_3_2_1_74_1","volume-title":"Local explanations via necessity and sufficiency: unifying theory and practice. arXiv preprint arXiv:2103.14651","author":"Watson David","year":"2021","unstructured":"David Watson, Limor Gultchin, Ankur Taly, and Luciano Floridi. 2021. Local explanations via necessity and sufficiency: unifying theory and practice. arXiv preprint arXiv:2103.14651 (2021)."},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389696"},{"key":"e_1_3_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3380571"}],"event":{"name":"SIGMOD\/PODS '22: International Conference on Management of Data","location":"Philadelphia PA USA","acronym":"SIGMOD\/PODS '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 2022 International Conference on Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514221.3522564","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3514221.3522564","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:07Z","timestamp":1750183807000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514221.3522564"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,10]]},"references-count":75,"alternative-id":["10.1145\/3514221.3522564","10.1145\/3514221"],"URL":"https:\/\/doi.org\/10.1145\/3514221.3522564","relation":{},"subject":[],"published":{"date-parts":[[2022,6,10]]},"assertion":[{"value":"2022-06-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}