{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:32:26Z","timestamp":1742913146779,"version":"3.40.3"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031711664"},{"type":"electronic","value":"9783031711671"}],"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\/978-3-031-71167-1_12","type":"book-chapter","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T13:02:58Z","timestamp":1725886978000},"page":"219-239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ULLER: A Unified Language for\u00a0Learning and\u00a0Reasoning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5502-4817","authenticated-orcid":false,"given":"Emile","family":"van Krieken","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1624-9188","authenticated-orcid":false,"given":"Samy","family":"Badreddine","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9907-7486","authenticated-orcid":false,"given":"Robin","family":"Manhaeve","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9313-753X","authenticated-orcid":false,"given":"Eleonora","family":"Giunchiglia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"doi-asserted-by":"publisher","unstructured":"Ahmed, K., et al.: PYLON: a pytorch framework for learning with constraints. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, 22 February\u20131 March 2022, pp. 13152\u201313154. AAAI Press (2022). https:\/\/doi.org\/10.1609\/AAAI.V36I11.21711","key":"12_CR1","DOI":"10.1609\/AAAI.V36I11.21711"},{"unstructured":"Ahmed, K., Teso, S., Chang, K., den Broeck, G.V., Vergari, A.: Semantic probabilistic layers for neuro-symbolic learning. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, 28 November\u20139 December 2022 (2022). http:\/\/papers.nips.cc\/paper_files \/paper\/2022\/hash\/c182ec594f38926b7fcb827635b9a8f4-Abstract-Conference.html","key":"12_CR2"},{"doi-asserted-by":"publisher","unstructured":"Aspis, Y., Broda, K., Lobo, J., Russo, A.: Embed2Sym - scalable neuro-symbolic reasoning via clustered embeddings. In: Proceedings of the Nineteenth International Conference on Principles of Knowledge Representation and Reasoning. International Joint Conferences on Artificial Intelligence Organization, Haifa, Israel, pp. 421\u2013431 (2022). https:\/\/doi.org\/10.24963\/kr.2022\/44","key":"12_CR3","DOI":"10.24963\/kr.2022\/44"},{"key":"12_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103649","volume":"303","author":"S Badreddine","year":"2022","unstructured":"Badreddine, S., d\u2019Avila Garcez, A., Serafini, L., Spranger, M.: Logic tensor networks. Artif. Intell. 303, 103649 (2022). https:\/\/doi.org\/10.1016\/j.artint.2021.103649","journal-title":"Artif. Intell."},{"unstructured":"Belle, V., Passerini, A., Van\u00a0den Broeck, G.: Probabilistic inference in hybrid domains by weighted model integration. In: Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), vol.\u00a02015, pp. 2770\u20132776 (2015)","key":"12_CR5"},{"doi-asserted-by":"crossref","unstructured":"Bingham, E., et al.: Pyro: deep universal probabilistic programming. J. Mach. Learn. Res. 20, 28:1\u201328:6 (2019)","key":"12_CR6","DOI":"10.1145\/3315508.3329974"},{"doi-asserted-by":"crossref","unstructured":"Carpenter, B., et al.: Stan: a probabilistic programming language. J. Stat. Softw. 76 (2017)","key":"12_CR7","DOI":"10.18637\/jss.v076.i01"},{"issue":"6","key":"12_CR8","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1016\/j.artint.2007.11.002","volume":"172","author":"M Chavira","year":"2008","unstructured":"Chavira, M., Darwiche, A.: On probabilistic inference by weighted model counting. Artif. Intell. 172(6), 772\u2013799 (2008). https:\/\/doi.org\/10.1016\/j.artint.2007.11.002","journal-title":"Artif. Intell."},{"unstructured":"Cohen, W.W.: TensorLog: a differentiable deductive database. arXiv:1605.06523 (2016)","key":"12_CR9"},{"doi-asserted-by":"publisher","unstructured":"Daniele, A., van Krieken, E., Serafini, L., van Harmelen, F.: Refining neural network predictions using background knowledge. Mach. Learn. 112(9), 3293\u20133331 (2023). https:\/\/doi.org\/10.1007\/S10994-023-06310-3","key":"12_CR10","DOI":"10.1007\/S10994-023-06310-3"},{"doi-asserted-by":"publisher","unstructured":"Darwiche, A.: SDD: a new canonical representation of propositional knowledge bases. In: IJCAI International Joint Conference on Artificial Intelligence, pp. 819\u2013826 (2011). https:\/\/doi.org\/10.5591\/978-1-57735-516-8\/IJCAI11-143","key":"12_CR11","DOI":"10.5591\/978-1-57735-516-8\/IJCAI11-143"},{"issue":"1","key":"12_CR12","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10994-015-5494-z","volume":"100","author":"L De Raedt","year":"2015","unstructured":"De Raedt, L., Kimmig, A.: Probabilistic (logic) programming concepts. Mach. Learn. 100(1), 5\u201347 (2015). https:\/\/doi.org\/10.1007\/s10994-015-5494-z","journal-title":"Mach. Learn."},{"doi-asserted-by":"publisher","unstructured":"De\u00a0Smet, L., et al.: Neural probabilistic logic programming in discrete-continuous domains (2023). https:\/\/doi.org\/10.48550\/arXiv.2303.04660","key":"12_CR13","DOI":"10.48550\/arXiv.2303.04660"},{"unstructured":"De\u00a0Smet, L., Sansone, E., Zuidberg Dos\u00a0Martires, P.: Differentiable sampling of categorical distributions using the catlog-derivative trick. Adv. Neural Inf. Process. Syst. 36 (2024)","key":"12_CR14"},{"doi-asserted-by":"crossref","unstructured":"Derkinderen, V., Manhaeve, R., Dos\u00a0Martires, P.Z., De\u00a0Raedt, L.: Semirings for probabilistic and neuro-symbolic logic programming. Int. J. Appro. Reason. 109130 (2024)","key":"12_CR15","DOI":"10.1016\/j.ijar.2024.109130"},{"key":"12_CR16","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.artint.2015.08.011","volume":"244","author":"M Diligenti","year":"2017","unstructured":"Diligenti, M., Gori, M., Sacca, C.: Semantic-based regularization for learning and inference. Artif. Intell. 244, 143\u2013165 (2017)","journal-title":"Artif. Intell."},{"unstructured":"Donoho, D.: Data science at the singularity. arXiv preprint arXiv:2310.00865 (2023)","key":"12_CR17"},{"unstructured":"Fischer, M., Balunovic, M., Drachsler-Cohen, D., Gehr, T., Zhang, C., Vechev, M.: Dl2: training and querying neural networks with logic. In: International Conference on Machine Learning, pp. 1931\u20131941. PMLR (2019)","key":"12_CR18"},{"unstructured":"Foerster, J., Farquhar, G., Al-Shedivat, M., Rockt\u00e4schel, T., Xing, E., Whiteson, S.: DiCE: the infinitely differentiable Monte Carlo estimator. In: International Conference on Machine Learning, pp. 1529\u20131538 (2018)","key":"12_CR19"},{"doi-asserted-by":"publisher","unstructured":"Giunchiglia, E., Stoian, M.C., Lukasiewicz, T.: Deep Learning with Logical Constraints. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23\u201329 July 2022, pp. 5478\u20135485. ijcai.org (2022). https:\/\/doi.org\/10.24963\/ijcai.2022\/767","key":"12_CR20","DOI":"10.24963\/ijcai.2022\/767"},{"doi-asserted-by":"publisher","unstructured":"Giunchiglia, E., Tatomir, A., Stoian, M.C.\u0103., Lukasiewicz, T.: CCN+: a neuro-symbolic framework for deep learning with requirements. Int. J. Appro. Reason. 109124 (2024). https:\/\/doi.org\/10.1016\/j.ijar.2024.109124","key":"12_CR21","DOI":"10.1016\/j.ijar.2024.109124"},{"doi-asserted-by":"crossref","unstructured":"Gordon, A.D., Henzinger, T.A., Nori, A.V., Rajamani, S.K.: Probabilistic programming. In: Future of Software Engineering Proceedings, pp. 167\u2013181 (2014)","key":"12_CR22","DOI":"10.1145\/2593882.2593900"},{"issue":"1\u20133","key":"12_CR23","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/0167-2789(90)90087-6","volume":"42","author":"S Harnad","year":"1990","unstructured":"Harnad, S.: The symbol grounding problem. Physica D 42(1\u20133), 335\u2013346 (1990)","journal-title":"Physica D"},{"unstructured":"Huang, J., et al.: Scallop: from probabilistic deductive databases to scalable differentiable reasoning. Adv. Neural Inf. Process. Syst. (2021)","key":"12_CR24"},{"key":"12_CR25","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.jal.2016.11.031","volume":"22","author":"A Kimmig","year":"2017","unstructured":"Kimmig, A., Van den Broeck, G., De Raedt, L.: Algebraic model counting. J. Appl. Log. 22, 46\u201362 (2017)","journal-title":"J. Appl. Log."},{"unstructured":"Kool, W., van Hoof, H., Welling, M.: Buy 4 REINFORCE samples, get a baseline for free!, p.\u00a014 (2019)","key":"12_CR26"},{"unstructured":"van Krieken, E., et al.: A-nesi: A scalable approximate method for probabilistic neurosymbolic inference. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, 10\u201316 December 2023 (2023). http:\/\/papers.nips.cc\/paper_files \/paper\/2023\/hash\/4d9944ab3330fe6af8efb9260aa9f307-Abstract-Conference.html","key":"12_CR27"},{"unstructured":"Maene, J., Raedt, L.D.: Soft-unification in deep probabilistic logic. In: Thirty-Seventh Conference on Neural Information Processing Systems (2023)","key":"12_CR28"},{"doi-asserted-by":"publisher","unstructured":"Magnini, M., Ciatto, G., Omicini, A.: On the design of PSyKI: a platform for symbolic knowledge injection into sub-symbolic predictors. In: Calvaresi, D., Najjar, A., Winikoff, M., Fr\u00e4\u00a0mling, K. (eds.) Explainable and Transparent AI and Multi-Agent Systems, vol. 13283, pp. 90\u2013108. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-15565-9-6","key":"12_CR29","DOI":"10.1007\/978-3-031-15565-9-6"},{"unstructured":"Manhaeve, R., Dumancic, S., Kimmig, A., Demeester, T., De\u00a0Raedt, L.: DeepProbLog: neural probabilistic logic programming. In: Proceedings of NeurIPS (2018)","key":"12_CR30"},{"unstructured":"Marra, G., Duman\u010di\u0107, S., Manhaeve, R., De\u00a0Raedt, L.: From statistical relational to neural symbolic artificial intelligence: a survey. arXiv:2108.11451 (2021)","key":"12_CR31"},{"key":"12_CR32","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/978-3-030-46147-8_17","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"G Marra","year":"2020","unstructured":"Marra, G., Giannini, F., Diligenti, M., Gori, M.: LYRICS: a general interface layer to integrate logic inference and deep learning. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11907, pp. 283\u2013298. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46147-8_17"},{"unstructured":"Marra, G., Ku\u017eelka, O.: Neural Markov logic networks. In: Uncertainty in Artificial Intelligence, pp. 908\u2013917. PMLR (2021)","key":"12_CR33"},{"doi-asserted-by":"publisher","unstructured":"Minervini, P., Riedel, S.: Adversarially regularising neural NLI models to integrate logical background knowledge. In: Korhonen, A., Titov, I. (eds.) Proceedings of the 22nd Conference on Computational Natural Language Learning, pp. 65\u201374. Association for Computational Linguistics, Brussels (2018). https:\/\/doi.org\/10.18653\/v1\/K18-1007. https:\/\/aclanthology.org\/K18-1007","key":"12_CR34","DOI":"10.18653\/v1\/K18-1007"},{"doi-asserted-by":"publisher","unstructured":"Pryor, C., Dickens, C., Augustine, E., Albalak, A., Wang, W.Y., Getoor, L.: NeuPSL: neural probabilistic soft logic. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pp. 4145\u20134153. International Joint Conferences on Artificial Intelligence Organization, Macau (2023). https:\/\/doi.org\/10.24963\/ijcai.2023\/461","key":"12_CR35","DOI":"10.24963\/ijcai.2023\/461"},{"doi-asserted-by":"publisher","unstructured":"Pryor, C., Dickens, C., Augustine, E., Albalak, A., Wang, W.Y., Getoor, L.: Neupsl: neural probabilistic soft logic. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, Macao, SAR, China, 19\u201325 August 2023, pp. 4145\u20134153. ijcai.org (2023). https:\/\/doi.org\/10.24963\/IJCAI.2023\/461","key":"12_CR36","DOI":"10.24963\/IJCAI.2023\/461"},{"issue":"1\u20132","key":"12_CR37","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10994-006-5833-1","volume":"62","author":"M Richardson","year":"2006","unstructured":"Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1\u20132), 107\u2013136 (2006). https:\/\/doi.org\/10.1007\/s10994-006-5833-1","journal-title":"Mach. Learn."},{"issue":"3","key":"12_CR38","doi-asserted-by":"publisher","first-page":"197","DOI":"10.3233\/AIC-210084","volume":"34","author":"MK Sarker","year":"2021","unstructured":"Sarker, M.K., Zhou, L., Eberhart, A., Hitzler, P.: Neuro-symbolic artificial intelligence. AI Commun. 34(3), 197\u2013209 (2021). https:\/\/doi.org\/10.3233\/AIC-210084","journal-title":"AI Commun."},{"unstructured":"Schulman, J., Heess, N., Weber, T., Abbeel, P.: Gradient estimation using stochastic computation graphs. Adv. Neural Inf. Process. Syst. (2015)","key":"12_CR39"},{"unstructured":"Siddharth, N., et al.: Learning disentangled representations with semi-supervised deep generative models. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 5927\u20135937. Curran Associates, Inc. (2017)","key":"12_CR40"},{"unstructured":"Skryagin, A., Stammer, W., Ochs, D., Dhami, D.S., Kersting, K.: SLASH: embracing probabilistic circuits into neural answer set programming. arXiv:2110.03395 (2021)","key":"12_CR41"},{"doi-asserted-by":"crossref","unstructured":"Slusarz, N., Komendantskaya, E., Daggitt, M.L., Stewart, R., Stark, K.: Logic of differentiable logics: towards a uniform semantics of dl. In: Proceedings of 24th International Conference on Logic, vol.\u00a094, pp. 473\u2013493 (2023)","key":"12_CR42","DOI":"10.29007\/c1nt"},{"doi-asserted-by":"crossref","unstructured":"Stol, M.C., Mileo, A.: Iid relaxation by logical expressivity: a research agenda for fitting logics to neurosymbolic requirements (2024)","key":"12_CR43","DOI":"10.1007\/978-3-031-71170-1_1"},{"unstructured":"Tang, Z., Hinnerichs, T., Peng, X., Zhang, X., Hoehndorf, R.: Falcon: faithful neural semantic entailment over alc ontologies. arXiv preprint arXiv:2208.07628 (2022)","key":"12_CR44"},{"unstructured":"Tang, Z., Pei, S., Peng, X., Zhuang, F., Zhang, X., Hoehndorf, R.: TAR: neural logical reasoning across TBox and ABox (2022)","key":"12_CR45"},{"doi-asserted-by":"crossref","unstructured":"Umili, E., Capobianco, R., De\u00a0Giacomo, G.: Grounding ltlf specifications in image sequences. In: Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning, vol.\u00a019, pp. 668\u2013678 (2023)","key":"12_CR46","DOI":"10.24963\/kr.2023\/65"},{"doi-asserted-by":"publisher","unstructured":"van de Meent, J.W., Paige, B., Yang, H., Wood, F.: An introduction to probabilistic programming (2021). https:\/\/doi.org\/10.48550\/arXiv.1809.10756","key":"12_CR47","DOI":"10.48550\/arXiv.1809.10756"},{"doi-asserted-by":"crossref","unstructured":"van Harmelen, F., ten Teije, A.: A boxology of design patterns for hybrid learning and reasoning systems. J. Web Eng. 18(1), 97\u2013124 (2019). https:\/\/doi.org\/10.13052\/jwe1540-9589.18133","key":"12_CR48","DOI":"10.13052\/jwe1540-9589.18133"},{"key":"12_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103602","volume":"302","author":"E van Krieken","year":"2022","unstructured":"van Krieken, E., Acar, E., van Harmelen, F.: Analyzing differentiable fuzzy logic operators. Artif. Intell. 302, 103602 (2022). https:\/\/doi.org\/10.1016\/j.artint.2021.103602","journal-title":"Artif. Intell."},{"unstructured":"van Krieken, E., Thanapalasingam, T., Tomczak, J., van Harmelen, F., Ten\u00a0Teije, A.: A-NeSI: a scalable approximate method for probabilistic neurosymbolic inference. In: Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems, vol.\u00a036, pp. 24586\u201324609. Curran Associates, Inc. (2023)","key":"12_CR50"},{"unstructured":"van Krieken, E., Tomczak, J., Ten\u00a0Teije, A.: Storchastic: a framework for general stochastic automatic differentiation. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol.\u00a034, pp. 7574\u20137587. Curran Associates, Inc. (2021)","key":"12_CR51"},{"doi-asserted-by":"crossref","unstructured":"Varnai, P., Dimarogonas, D.V.: On robustness metrics for learning STL tasks. In: 2020 American Control Conference (ACC), pp. 5394\u20135399. IEEE (2020)","key":"12_CR52","DOI":"10.23919\/ACC45564.2020.9147692"},{"issue":"1","key":"12_CR53","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1109\/TKDE.2021.3079836","volume":"35","author":"L von Rueden","year":"2023","unstructured":"von Rueden, L., et al.: Informed machine learning - a taxonomy and survey of integrating prior knowledge into learning systems. IEEE Trans. Knowl. Data Eng. 35(1), 614\u2013633 (2023). https:\/\/doi.org\/10.1109\/TKDE.2021.3079836","journal-title":"IEEE Trans. Knowl. Data Eng."},{"doi-asserted-by":"crossref","unstructured":"Winters, T., Marra, G., Manhaeve, R., De\u00a0Raedt, L.: Deepstochlog: neural stochastic logic programming. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a036, pp. 10090\u201310100 (2022)","key":"12_CR54","DOI":"10.1609\/aaai.v36i9.21248"},{"unstructured":"Wu, X., Zhu, X., Zhao, Y., Dai, X.: Differentiable Fuzzy $$\\cal{ALC}$$: a neural-symbolic representation language for symbol grounding (2022)","key":"12_CR55"},{"unstructured":"Xu, J., Zhang, Z., Friedman, T., Liang, Y., den Broeck, G.V.: A semantic loss function for deep learning with symbolic knowledge. In: Dy, J.G., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, 10\u201315 July 2018. Proceedings of Machine Learning Research, vol.\u00a080, pp. 5498\u20135507. PMLR (2018). http:\/\/proceedings.mlr.press\/v80\/xu18h.html","key":"12_CR56"},{"doi-asserted-by":"publisher","unstructured":"Yang, Z., Ishay, A., Lee, J.: NeurASP: embracing neural networks into answer set programming. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pp. 1755\u20131762. International Joint Conferences on Artificial Intelligence Organization (2020). https:\/\/doi.org\/10.24963\/ijcai.2020\/243","key":"12_CR57","DOI":"10.24963\/ijcai.2020\/243"}],"container-title":["Lecture Notes in Computer Science","Neural-Symbolic Learning and Reasoning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-71167-1_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T22:37:24Z","timestamp":1732747044000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-71167-1_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031711664","9783031711671"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-71167-1_12","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":"10 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NeSy","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural-Symbolic Learning and Reasoning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Barcelona","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","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":"9 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"nesy2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/nesy2023","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}